NEVADA CITY, Calif.—Media workflow technology provider Telestream, has announced major new AI-powered features in its product portfolio, with practical AI enhancements across its Vantage, Vantage Cloud, EDC, Stanza, and Qualify product lines.

Building on the foundation introduced with Vantage AI in 2025, Telestream said it is is advancing its production-ready AI workflows, accelerating localization, strengthening quality control, and introducing new AI-assisted workflow design capabilities that connect and unify enterprise operations across on-premises, cloud, and hybrid environments.

“Last year we introduced Vantage AI as a new foundation for intelligent media workflows,” said Rich Andes, vice president of product management at Telestream. “Today, we’re expanding that foundation with deeper integration across cloud and hybrid deployments, enhanced QC automation, and scalable localization workflows. This is practical AI in action: embedded, explainable, and designed for real-world production environments.”

The latest enhancements reflect a shift from introducing AI features to operationalizing AI across the enterprise, driving measurable workflow impact across ingest, processing, compliance, and delivery.

Telestream reported that its latest AI advancements deliver meaningful operational progress across five core areas:

• AI Caption: Expanded Localization at Scale. AI-powered captioning and translation now support up to 128 languages across Vantage and Stanza workflows, enabling automated speech-to-text, caption generation, and multilingual subtitle delivery in a unified processing chain. These enhancements significantly reduce turnaround time for FAST channels, OTT distribution, and global sports syndication.
• AI Qualify: AI-Assisted QC Embedded in Production Pipelines. Enhancements to Qualify integrate AI-powered lip-sync validation, subtitle alignment checks, and spoken-language verification directly into automated QC workflows. By flagging only exception-based issues for operator review, teams reduce manual inspection time while improving delivery confidence and platform compliance.
• AI Speech: Real-Time Speech Intelligence for Growing Files. Speech-to-text and metadata extraction capabilities now support growing file workflows, enabling real-time tagging and searchable metadata during ingest and processing. This allows news, sports, and live production teams to accelerate time-to-edit and improve content discoverability without waiting for file completion.
• AI Vision: Visual Intelligence Reduces Manual Lifting. AI Vision introduces frame-level visual intelligence across Telestream workflows, automatically transforming video into actionable metadata that extends beyond traditional file properties. Using advanced computer vision, AI Vision identifies objects, logos, lower thirds, safe-area violations, and objectionable content, while generating scene-level descriptions in real time. These capabilities enable describing the scene within a video feed as metadata, logo detection and removal across large content libraries, and ad-break identification and replacement for VOD repurposing. AI Vision reduces manual review time, strengthens compliance workflows, and expands automation for live and file-based production environments.
• AI Media Analyzer: Content-Aware Analysis. AI Media Analyzer advances content inspection from basic technical validation to contextual understanding of what is actually inside a media file. Rather than simply verifying resolution, frame rate, or codec, Media Analyzer detects the nature of the content, segments media into meaningful sections, and generates metadata that reflects scene changes, content types, graphic overlays, and potential compliance risks. This content-aware analysis enables faster review cycles and more accurate downstream workflow triggering. It provides significant operator time savings across ingest, processing, and delivery environments, transforming media assessment into an automated, production-ready capability. This feature is available in Telestream's EDC platform and will be coming soon to Vantage AI.

These enhancements ensure AI capabilities are integrated components that create a trusted production infrastructure.

“AI delivers value when it’s operationalized at scale,” Andes added. “Our customers don’t need more labels or transcripts; they need intelligent automation that reduces turnaround time, improves accuracy, meets compliance, and scales with their infrastructure and workflows. That’s what this next phase of Practical AI delivers.”

As with previous releases, Telestream’s AI capabilities remain secure by design. Customers retain full control of their content, whether deployed on-premises, in private environments, or via managed cloud services. No customer media is used to train shared models, ensuring compliance, auditability, and cost predictability.

In addition to AI workflow enhancements, Telestream also reported that it continues to invest in next-generation format readiness and compliance standards, including support for emerging codecs and audio standards and exploration of C2PA metadata workflows to address growing content authenticity requirements.

To learn more about Telestream’s production-ready AI, visit https://www.telestream.com/vantage-ai/.

BRUSSELS—Mediagenix has announced that it has joined the Amazon Web Services (AWS) Independent Software Vendor (ISV) Accelerate Program (ISV). This acceptance follows the successful completion of the AWS Foundational Technical Review (FTR) across Mediagenix platforms that underpin the company’s Content Strategy and Budgeting, Content Value Management for title and rights metadata, and Content Scheduling solutions.

“Joining the AWS ISV Accelerate Program connects Mediagenix to the AWS sales organization, and enables frictionless procurement of our solutions through the AWS Marketplace,” said Emmanuel Müller, CPO, Mediagenix.“Successfully completing the AWS Foundational Technical Review is a powerful validation of our platforms and business. Together, these achievements reinforce our commitment to delivering secure, reliable, and innovative solutions while expanding opportunities to work more closely with AWS to serve customers worldwide.”

The AWS ISV Accelerate Program connects independent software vendors directly with AWS field teams, enabling streamlined engagement with customers worldwide. Mediagenix will be able to simplify procurement through AWS Marketplace, align more closely with AWS go-to-market initiatives, and accelerate the delivery of its solutions to enterprise customers, the company reported.

Passing the AWS FTR validates Mediagenix platforms for their adherence to AWS Well-Architected best practices in areas like security and reliability. The process requires proactive risk analysis and mitigation, ensuring Mediagenix solutions are designed to perform at scale in alignment with AWS standards, the company explained.

Building on the integration of Spideo personalization and recommendations technology and the introduction of the Self-Optimizing Content Monetization Flywheel, Mediagenix also reported that it continues to expand the intelligence across its solutions and bring value to its customers.

With recent high marks in security credentials, including SOC 2 Type II compliance and the recertification of ISO 27001, Mediagenix further demonstrates its dedication to safeguarding customer trust. Achieving AWS FTR and joining the AWS ISV Accelerate Program underscores Mediagenix’s commitment to technical excellence while enhancing opportunities to deepen customer engagement, the company said.

Mediagenix’s Humanized Semantic Search was recently awarded IBC2025 Best of Show by TV Tech, while its Scheduling Automation solution captured two IBC2025 Best of Show honors, one from TV Tech and one from TVBEurope.

The streaming revolution has unlocked boundless opportunities for content creators and distributors, but it’s also created a new kind of chaos. As libraries expand across geographies, platforms and formats, media companies are discovering that their most valuable digital assets are only as powerful as the data that defines them.

According to a 2025 Publishing Meta article, up to 40% of licensing revenue is lost due to poor metadata. That statistic alone should serve as a wake-up call for anyone who relies on intellectual property to drive business growth. In an era of infinite content, metadata has become the new master. The ability to classify, track and activate media assets through accurate metadata isn’t just a technical requirement — it’s a strategic imperative.

The Hidden Value of Proper Tagging
Too often, metadata is treated as a back-office task — a functional necessity for organizing titles. In reality, it’s one of the most potent business assets in the media ecosystem and should be treated as a core component of strategy to avoid cutting off monetization opportunities.

Rich, consistent tagging powers everything from content discovery and personalized recommendations to global distribution and rights management. For streamers, it directly influences viewer retention and engagement. For studios, it determines how quickly and effectively content can be licensed or repurposed.

When metadata is incomplete or inconsistent, opportunities are lost. One global entertainment company learned this firsthand when tasked with organizing more than 1,000 titles for an international launch in just 30 days. Without unified metadata standards and robust tagging, even world-class content risks getting lost in translation.

According to a report by the United States Copyright Office, industry experts often describe metadata as “the linchpin, the DNA of everything driving ROI in streaming,” emphasizing that accurate, rich metadata is the key factor enabling higher returns for over-the-top (OTT) and free ad-supported streaming TV (FAST) platforms.

The AI Dilemma: Promise vs. Performance
AI has become an indispensable tool in content management, capable of auto-tagging assets, identifying faces, transcribing dialogue and detecting objects. Yet while AI accelerates the process, it doesn’t replace the need for human context and quality assurance. Algorithms can recognize what’s in a frame, but not always why it matters. They can tag a character but miss the narrative relevance. That’s where human expertise bridges the gap.

For instance, an algorithm may tag a scene as ‘car chase’ but miss its significance in character development or sponsorship potential. Professional metadata services combine AI-driven scale with human validation, ensuring accuracy, relevance and alignment with evolving business models. This hybrid model transforms metadata from a dataset into a decision-making asset.

Monetization Challenges in the Streaming Landscape
Today’s media organizations face a paradox of abundance: more content than ever, but growing difficulty monetizing it. Fragmented archives, inconsistent metadata and siloed systems make it harder to know what’s owned, where it lives and how it can be used.

Poor metadata management can cost millions in delayed rights clearances, missed licensing opportunities or underutilized catalogs. PublishingMeta further reports that bad, inconsistent or incomplete metadata leads to subscriber attrition, lost monetization opportunities and inefficiencies in finding and using media assets — all of which directly affect revenue and strategic positioning.

Conversely, precise metadata unlocks:

• More effective licensing: Rapid identification of assets ready for syndication or regional reuse.
• Improved international distribution: Consistent tagging that aligns with local compliance and cultural context.
• Enhanced recommendation systems: More accurate personalization that drives viewer engagement and retention.
• Metadata fuels monetization: It turns static archives into living assets that continuously generate value.

Future of Content Management & Conclusion
As the streaming landscape evolves, so will expectations around metadata. Wired reports that Netflix claims 80% of TV show views are discovered through metadata-driven recommendations, a direct illustration of metadata’s impact on user engagement and content consumption.

Emerging technologies — from AI tagging assistants to rights-tracking ledgers — will continue to reshape how content is described, distributed and discovered. Yet even as automation advances, human expertise remains the differentiator. Media professionals who understand both the art and the architecture of content will continue to play a critical role in maximizing value and ensuring integrity.

For forward-looking media companies, the mandate is clear:

• Invest in metadata quality as a strategic priority.
• Blend automation with human intelligence.
• Treat tagging as the foundation of monetization, not a technical afterthought.

In a crowded marketplace, metadata isn’t just information about your content — it is your content’s competitive edge.

We all agree that content isn’t valuable unless it’s accessible and that access today (and for the foreseeable future) depends almost entirely on metadata. And while AI tools have made headlines for sensational visual generation, the real opportunity lies in applying AI at the point of ingest, where metadata can be structured, system-ready, and immediately usable across production, editorial and asset management systems.

For organizations managing mixed environments that include OGs like Avid to newcomers like iconik and Mimir, the challenge is consistent: how to generate actionable metadata once, and make it usable everywhere.

The answer requires more than transcription. It requires engineering metadata pipelines that are system-aware, and automation-ready.

The Metadata Challenge at Ingest
Ingest workflows today must accommodate a wide range of formats and signal types, from SDI and SRT to ST 2110 and file-based delivery. These inputs often arrive without standardized metadata, or worse, with metadata that is incompatible with the production environment it’s entering.

When your production, editorial or asset management system can’t interpret this metadata, assets become invisible. They might be present on shared storage or in a database, but they are effectively inaccessible to your colleagues who rely on search, tags, and categorization to do their work.

This isn’t just a usability issue; It is a systemic bottleneck—one that leads to duplicating efforts, unnecessary storage bloat, and unused assets. The manual update of metadata can be a drag on the creative process, and the big downside, loss of monetization opportunities around unused assets.

The Real World is a Mix of Logos
Consider a media operation where the editorial team builds long-form stories in Avid Media Composer, the digital publishing team uses iconik for clipping and social content, and leadership is planning a move to a cloud-native MAM like Mimir. Without metadata interoperability, these systems operate as isolated silos.

Engineers in this environment face several challenges:

• How do you normalize metadata formats across systems with different schemas?
• How do you automate tagging without exposing sensitive data to external AI services?

How do you build a metadata layer that can persist across platform migrations?

Solving these problems starts at ingest. When metadata is created during ingest using a controlled, customizable AI engine, it becomes possible to prepopulate editorial and asset management systems with structured, validated, and consistent metadata.

Privacy Please
Security and privacy is paramount for every professional media company. They want on-premise, media-specific AI engines that can generate multiple types of text-based metadata such as:

• Transcripts for full-text indexing and search
• Sub-clip summaries for segment-level context
• File-level summaries for cataloging and archive tagging
• Sentiment tags for editorial filtering

Keyword Extraction for Named Entities and Events
Because the AI runs entirely within the local infrastructure, there is no need for cloud access, no exposure of sensitive content, and no unpredictable usage-based costs. The output can be formatted to match the ingest requirements of your system without risk of your data being scanned or used to train public data sets.

The result is an ingest workflow that not only delivers transcoded media, but a complete metadata envelope with zero risk that is ready for immediate use across platforms.

Building a System-Aware Metadata Pipeline
The critical engineering task is not just metadata creation. It’s metadata alignment. AI-generated metadata must be:

• Standards-compliant to ensure compatibility across ingest and editorial systems
• Contextually aware so that summaries and tags reflect the intended use of the asset
• Mapped to system-specific schemas, including AAF for Avid or JSON/XML for cloud-based asset managers
• Linked via persistent identifiers to ensure traceability across edits and platform migrations

When this is done correctly, it unlocks a number of efficiencies. Editorial teams can search by phrase or keyword without transcribing manually. Digital teams can find content aligned to tone, event, or subject matter. Engineers can integrate new platforms without retrofitting metadata after migration.

Most importantly, content becomes discoverable and usable immediately upon ingest rather than sitting dormant on storage until it’s manually processed (or not).

Engineering for Discoverability
From an engineering standpoint, this is about shifting metadata from a post-processing task to an ingest-native function. When AI is embedded directly into the ingest workflow, metadata generation becomes deterministic, repeatable, and secure. The pipeline delivers not just media assets, but indexed, searchable, and categorized content across the production stack.

This is not just a smart automation play. It is a foundational shift in how media workflows are engineered for speed, scale, and cross-platform utility. In a landscape where discoverability drives reuse, speed to air, and monetization, metadata is no longer optional infrastructure. It is core to system design.

As CTV continues its ascent, advertisers are becoming increasingly frustrated by opaque practices around content metadata. Agencies and tech providers want to know exactly where their ads appear, but many publishers remain hesitant, citing privacy regulations such as the Video Privacy Protection Act (VPPA) as justification for withholding detailed content information.

This standoff has led advertisers to pursue unconventional—and potentially risky—methods of gaining insight into content environments, raising serious questions about the future of transparency in CTV advertising.

Where's the Money Going?
Transparency is becoming central to performance marketing on CTV. As advertisers shift greater shares of their budgets toward streaming, they demand clarity on where and how their dollars are being spent. Yet, publishers often offer limited metadata—usually restricted to broad genre-level or network-level reporting.

Advertisers are left guessing whether their content is appearing next to premium, brand-safe shows, or lesser-quality inventory relegated to "performance CTV," a euphemism often reserved for remnant inventory with lower engagement and effectiveness.

The lack of transparency is reminiscent of the early days of digital display advertising, where publishers similarly guarded inventory data closely. But digital display evolved, driven by demands for openness from advertisers, ultimately moving toward a more programmatic, transparent ecosystem. CTV is now at a similar crossroads.

CTV publishers frequently argue they're protecting user privacy by withholding detailed content-level metadata, invoking the VPPA, which prohibits disclosure of personally identifiable viewing data. While privacy concerns are valid, advertisers question whether VPPA concerns are being overstated or used as a convenient shield against transparency.

After all, advertisers aren't seeking individual user data—they simply want granular content-level metadata. This metadata doesn't compromise viewer privacy when provided in aggregate; instead, it supports better ad placement, brand alignment, and performance.

Advertiser Desperation
In response to publisher reticence, agencies and technology providers have started experimenting with indirect methods to uncover content metadata. One tactic involves combining users’ location data with electronic program guides from virtual MVPDs to infer which shows viewers might be watching.

This workaround underscores advertiser desperation, but it's a risky, unsustainable practice. If advertisers begin routinely exploiting indirect techniques to unearth data publishers refuse to share, trust will erode on all sides, fueling tension and potentially triggering stricter regulatory scrutiny.

Publishers must recognize that limited transparency actively undermines trust in CTV. Transparency ultimately benefits publishers too, attracting larger ad budgets and deeper investments from brands eager for reliability and consistency. Moreover, refusing to share content metadata won't stop advertisers from pursuing alternatives. Instead, publishers should find balanced ways to disclose greater granularity.

Netflix and Hulu offer instructive examples. Initially wary of transparency, both platforms have incrementally embraced more programmatic ad-buying practices, gradually offering richer metadata and increased advertiser control. Their approach demonstrates that transparency need not conflict with viewer privacy or business interests—it can enhance both.

Looking forward, publishers must decide: hold firm, risking advertiser backlash and fragmented practices, or open up metadata more fully, creating a robust, transparent, and sustainable CTV ecosystem. Agencies, brands, and technology providers have shown they're willing to push boundaries in pursuit of transparency. Rather than forcing their hand, publishers have the opportunity to step up proactively, balancing legitimate privacy concerns with meaningful transparency.

The future of CTV relies heavily on trust, precision targeting, and performance metrics - all impossible without clear, detailed, and accessible metadata. It's time for publishers to give some ground, before advertisers force the issue in ways that benefit no one.

LISBON—NDI, the global standard for plug-and-play connectivity, is launching an initiative to enhance NDI metadata capabilities in ways that will unlock unprecedented control in live production and broadcast workflows.

As part of that effort, it has released updated documentation details on how to leverage seamless, bidirectional XML metadata exchange between NDI senders and receivers to enable new workflows, remote control, and smarter automation for live events and broadcast environments.

Besides documenting some already known capabilities with the support of Vizrt, the new release also introduces four new official standard metadata elements, developed in collaboration with some of the most innovative brands in the NDI Ecosystem. Those include: support for broadcast standards CEA-708 & SCTE-104, with ToolsOnAir, the audio standard MIDI with LAMA, and the lighting control standard DMX, in partnership with SalrayWorks.

"One of the greatest advantages of IP video is not just the ability to transmit video over UTP cables, but the integration of advanced functionalities like seamless device connectivity, remote control and firmware updates," said Geen Jung, CEO and founder at SalrayWorks. "Over the years, NDI has consistently fostered an ecosystem that inspires innovation which for SalrayWorks, has opened new possibilities in fields such as lighting and audio.”

Traditionally, live events require separate teams to manage video, lighting, and audio, which can be complex and costly. This is particularly true for smaller organizations like churches or school sporting events. Using NDI-based setups to transport metadata enables seamless remote management of each component within a single, unified workflow, it said.

For instance, integrating NDI with lighting systems allows for real-time, remote adjustments directly from video preview screens, eliminating the need for a dedicated specialist. This transforms live production environments, empowering broadcasters to handle complex operations with ease with professional-grade automation.

“Having processed metadata across all our solutions for years—primarily in the SDI domain—we recognize its critical role in broadcast production. NDI metadata extends this capability into the IP realm, enabling deeper integration, smarter automation, and more efficient workflows within the NDI ecosystem. The excellent and solution-oriented collaboration with the NDI team ensures that together, we push the boundaries of video innovation, creating seamless, intelligent solutions for the present and future of NDI-based productions,” said Manuel Grund, COO of ToolsOnAir.

“NDI metadata enables us to integrate camera tracking data within the same stream where audio and video are transported. This ensures that tracking data remains synchronized with the video frame, regardless of transmission delays,” said Markus Rainer, software architect at Vizrt. “At Vizrt, our PTZ cameras already embed tracking data directly, while other solutions utilize the NDI Advanced SDK to embed externally delivered tracking data before transmission. By centralizing metadata documentation, NDI provides a unified resource for managing metadata extensions. This allows us to seamlessly reference existing solutions, reducing redundancy.”

The success of these partnerships inspired the creation of a new initiative, the Metadata Lab. Any brands and developers that want to follow in the footsteps of companies like LAMA, can now submit their metadata proposal at ndi.video/metadata-lab. The NDI technical team will then review proposals and work together with creators to test and improve on the submission. When ready, NDI will publish it as an official NDI Metadata standard in the ever-growing documentation. As new metadata integrations expand across the ecosystem, broadcasters and production teams will have more access to unparalleled flexibility and control, unlocking new possibilities for the future of live production.

“The LAMA team is excited to work with NDI to explore the possibilities of NDI metadata. Our existing implementation in LAMA Connect already makes our customer's workflows simpler, faster, and more efficient. This is only the beginning, and we look forward to seeing where we can take this technology,” said Ewan Cameron, CEO and Co-founder at Lama.

LOS ANGELES—Xytech Systems, a provider of media resource management software, has announced a merger with Fabric, a developer of media supply chain and metadata management solutions. The name of the new company will be announced at a later date and will be led by Fabric CEO, Rob Delf. Xytech’s current CEO, John O’Connor will become Chief Operating Officer.

The two companies are targeting the expanding market for data-based media resource management and supply chain-based media production with Xytech known for its comprehensive media resource management solutions including asset management, localization, formatting, packaging and transmission services and Fabric’s metadata and supply chain management tools.

By integrating their technologies and expertise, the new entity “will deliver innovation and efficiencies to media organizations in producing, managing, and distributing their content,” the company said.

“To me, that is a really big opportunity to move the needle forward and be more technology-focused so the architects of the supply chains are saying Xytech will work for us for the future, while at the same time preserving this very specific functionality that the industry’s relied on for 30 years,” Delf told TV Tech sister brand TVBEurope. “That’s the purpose of the merger.”

The two companies expect the merger to deliver substantial customer value by reducing time spent on media management tasks. The combined solution will feature greater automation and data handling, allowing organizations to quickly access and manage their data and resources with minimal manual intervention. This efficiency translates into faster decision-making and a more agile response to market demands, according to the company.

“Fabric’s award-winning media supply chain and metadata management solutions complement Xytech Systems’ powerful resource management tools perfectly,” said John O’Connor, CEO of Xytech Systems. “Together, we’ll provide our customers with powerful tools that simplify their workflows and accelerate their business processes.”

Private equity firm Banneker Partners, which specializes in investing in resource management and supply chain management tools, acquired Xytech in 2021.

“We are excited about the transformative merger between Xytech and Fabric, a partnership poised to redefine the standards in media resource and data management,” said Kenneth Frank, Partner at Banneker Partners. “By uniting their strengths, we are merging mission-critical technologies with the industry’s leading management team to pave the way for the innovation that Xytech and Fabric customers need for the future. This isn’t just about combining companies; it’s about creating an exceptional, data-driven, and future-focused media and entertainment platform.”

In the face of a highly competitive industry and ongoing competitive nature of the industry, media companies today are strongly incentivized to make use of their archive content. However, many have been stalled in their attempts to leverage archive content by their libraries' sheer volume and complexity. 

Broadcasters may have several decades of unorganized material to sort through and furthermore, said companies’ content may still reside on physical media (e.g., tapes), thus needing to be digitized.

Metadata—simply put, a set of data providing information about other data—can enhance content, providing useful descriptive information, such as the individuals featured, the location of filming, the narrative context, and more advanced information, such as copyrights, expiration dates, and audio descriptions. It can also aid this organizational process. 

Thus, we can understand that the money and time-consuming task of digitizing and organizing archive content can be much more purposeful if metadata is added during and after the digitizing process. This ensures the organization process is streamlined, and that media and entertainment companies’ content is in its most advantageous form, with enhanced discoverability, allowing them to stay ahead in the competitive landscape of digital streaming and production services.

Clearing the Way
Metadata is becoming increasingly important for all media companies due to its improved consistency, data quality and cost efficiency. Its value needs to be utilized across the value chain, where different systems have different metadata requirements due to applications, such as a Content Management System (CMS) and within a production environment via a Product Asset Management system (PAM). 

Without establishing or implementing metadata management within a coherent business strategy, media companies could find themselves at risk of making costly mistakes and missing out on new monetization opportunities. 

One way they have been doing this is through metadata enhancement. Enhancing or enriching metadata is a method used to boost the value and discoverability of digitized content. This involves expanding the existing metadata associated with the content. Moreover, AI-assisted processes can fully automate the addition of extra metadata. 

For instance, in sports content, enhanced metadata might involve tagging individual players, specific types of plays, and real-time score updates, among other details. The primary aim is to facilitate efficient and swift content searches later on, thereby encouraging greater utilization of the available content.

Status Quo Opportunity Costs
Opportunity costs are difficult to quantify, but it’s worth assessing the ROI of metadata capabilities in the context of a highly competitive and fast-moving industry. Media companies that fail to maximize their resources with metadata enhancement put themselves at a competitive disadvantage. 

The media and entertainment industry’s business models are increasingly geared towards diversified monetization strategies, and archive content provides the raw materials to expand your offering. Many sports broadcasters have already caught on to the value of archive material, such as highlights from past matches, to reward dedicated fans and maintain engagement between seasons. The same principle applies universally: having more content ready at your disposal reduces the likelihood of churn and puts you ahead of the competition. 

Setting the Stage for Seamless Collaboration
With a metadata-rich library at their disposal, production teams will have a highly accessible bank of content to work with. This is one of the key differentiators among media companies in 2024: how well are their systems optimized for collaboration? Content that has been indexed through metadata is easily shared and accessed across remote or asynchronous teams. 

The speed and complexity of modern production and distribution may have increased, but metadata will enable teams to zero in on whatever files they need to find at a moment’s notice. Whatever challenges production teams face, ‘needle in a haystack’ situations can be a thing of the past with the assistance of metadata. 

Carpe Datum
Now is the time to embrace metadata. It offers immediate benefits and a long-term ROI that includes business agility, revenue opportunities, and highly-specialized use cases to suit individual content strategies. Production teams gain access to an indexed library that positions everything they need within arm’s reach, empowering them to make full use of their resources. 

With that said, the technology is still new, and we may only be seeing the tip of the iceberg for what metadata has to offer. With the support of a trusted tech provider, metadata can be one of the essential tools that empowers broadcasters to stay ahead of the curve.

LONDON—MetaBroadcast, the UK's leading metadata management specialist, and Nielsen’s Gracenote have announced that they have been awarded a three year contract renewal by Barb, the U.K. audience measurement service, to operate its combined content ID and genre allocation system from January 2025.

Barb is the main provider of TV measurement in the U.K. 

As part of the deal, MetaBroadcast will use industry-leading Gracenote schedule metadata and unique content identifiers to reduce timescales for accurate programme identification, validation, enrichment and genre classification, as well as improve coverage and accuracy of broadcaster IDs. 

The inclusion of Gracenote will also allow Barb to better deduplicate content and accelerate reporting from seven days to three days after programmes are first made available on linear channels or streaming services.

"MetaBroadcast is delighted to continue supporting Barb by leveraging the robust metadata management capabilities of Atlas, our cloud-based active data platform, and by now working with premier global metadata provider Gracenote to improve content ID coverage and classification," said Jamie Mackinlay, CEO, MetaBroadcast. "We recognise the importance of accurate, consistent, and timely programme data that allows Barb to fulfill the needs of its audience measurement customer base. We look forward to continuing our collaboration."

"By leveraging Gracenote's unique content IDs and programme metadata, measurement providers can quickly identify and better understand what people are watching across different services and platforms," said Tim Cutting, chief revenue officer at Gracenote. "We look forward to working with MetaBroadcast and enabling Barb to deliver new levels of speed, accuracy, consistency and completeness in their multi-screen audience measurement."

Barb's audience measurement relies on a system for identifying and classifying traditional linear broadcast, catch up or streaming viewing. Using Gracenote data as a key system input will ensure consistent programme titles and genres, accurate connections between programmes and episodes and linkages to broadcasters' linear and streaming content across platforms, the companies added. 

LOS ANGELES—The Hollywood Professional Association (HPA) has announced program details for the HPA Tech Retreat, Feb. 18-22, 2024, at the Westin Rancho Mirage Golf Resort & Spa in Rancho Mirage, Calif.

The retreat consists of three parts: TR-X, Feb. 19, which focuses on Extreme Production; the Supersession, Feb. 20, (details will be announced later); and the main conference program on Feb. 21-22, HPA said.

“Each year we get many more outstanding submissions than time allows in the program, but this year was unprecedented,” said Mark Schubin, who has guided the main conference program for more than 20 years. “There wasn’t a dud in the bunch; it was really hard to choose the most important ones. We’ve ended up with presentations that run the gamut from a graduate student to a major C-level executive to top creatives in the field.”

For Wednesday., Feb. 21, the program includes: 

• What CES 2024 Means for the Media Industry—Mark Harrison, DPP.
• Quantum Dots and Color Imaging—Charles Poynton.
• The Death of Analog: Why Right Now Is the Time to Digitize Your Archive—moderated by Carin Forman, Amazon; with Heidi Shakespeare, Memnon; and Andrea Kalas, Paramount.
• New Developments in Open Source Software for the Motion Picture Industry—David Morin, Academy Software Foundation.

Also on Weds. Feb. 21, the program includes "Revolutionizing Media Production: AI's Role from Conceptualization to Final Cut." This programming includes: 

• Setting the Stage for AI. (AI & Frontier Technologies in the Age of Disruption: A Foundation in Embracing Change for Progress—Daniel Doll-Steinberg, EdenBase; AI for Media: What does good look like?—Mark Harrison, DPP; The Computable Studio: A Vision for 2025 and Beyond—Yves Bergquist, Entertainment Technology Center, University of Southern California; and Setting the Stage for AI Discussion and Q&A.
• AI and Creativity. (AI and the Future of Cinematic Storytelling—Barbara Ford Grant; Studio Perspective: AI and Content Creation—Tony Guarino, Paramount; Using GenAI as a Screenwriting Partner and Preproduction Assistant—Christian Cantrell, Concept.art; OK, We Have AI Tools and AI Content. What About the Business? Bringing It All Together for Successful Future State—Caleb Ward, Curious Refuge; AI and Creativity Discussion and Q&A). 
• AI in the Workflows. (Enhancing Creative Decisions with AI Generated Content. Recommendations—David Colantuoni, Avid; Multi-Language Generation of Movie Content — Siegfried Fößel, Fraunhofer-Institute for Integrated Circuits IIS; Demystifying AI in the Supply Chain — Pinar Seyhan Demirdag, Cuebric; AI in the Workflows discussion and Q&A). 

Programming for Thursday Feb. 22 includes:  

• MovieLabs 2030 Update
• The State of the Vision (Where Are We on the Path to 2030?—Mark Turner, MovieLabs; Bringing the “Studio in the Cloud” to Fruition—Mark Turner, MovieLabs; Richard Duke, Avid; Implementing the MovieLabs Ontology for Media Creation-How & Why—Mark Turner, MovieLabs; Guillaume Rachez, Perfect Memory; Workflows Based on 2030 Vision—Mark Turner, MovieLabs; Aki Seino, Imagica Entertainment Media Services.)
• Metadata Mania: Can We Create a Common Source of Truth? (Introduction: The Challenges of Metadata—Jim Helman, MovieLabs; Our Flag Means Death Season 2—Utilizing MovieLabs OMC for Metadata—Mike Urban, The Rebel Fleet; OpenMetadataIO, an Open Standards Initiative — Jim Helman, MovieLabs; Toby Scales, Google; Dalia Spingarn, Paramount Global). Interoperability: A Key to the Creative Benefits of the 2030 Vision. (Interoperability 101—Jim Helman, MovieLabs; Interoperability panel—Moderator: Jim Helman, MovieLabs; Panelists to be added).
• 2030 Vision Q&A (Stepping Up to Color Managed On-set HDR/SDR Workflows—Moderator: Michael Chambliss, International Cinematographers Guild; Greg Smokler, Small HD; Responsible Innovation in the Age of Generative AI—Ely Greenfield, Adobe; Color Appearance Models and the future of Color Management: How the industry Working Group for the new Developer Release of ACES 2.0 investigated, debated and ultimately embraced CAMs for its new output transform—Alex Forsythe, Academy of Motion Picture Arts and Sciences; NeRFs for Nerds: Another Future for Creativity—Erik Weaver, Entertainment Technology Center at USC; Eric Pohl, Singularity Imaging; Alex Porter, Mod Tech Labs; Addy Ghani, disguise; Building the Studio of Tomorrow, Today!—Mary Palmer, Amazon; Let’s Have an Honest Conversation about Sound—Nuno Fonseca, Sound Particles; Untwisting the Rope of Character Identification—Holly Beavon, Fielding Graduate University)

Registration for the retreat is available online.

AMSTERDAM—Nielsen’s Gracenote unit has unveiled a new offering to help content publishers and broadcasters get their Free Ad-Supported Streaming TV (FAST) channels and programming distributed by major streaming platforms more easily. 

The Gracenote FAST Program, which Gracenote will be showing during IBC2023, uses advanced data enrichment services to help Gracenote customers accelerate time-to-market for their FAST offerings.

“The rapid rise of FAST has been one of the most notable recent developments in the global video ecosystem,” said Vikram Kulkarni, vice president, strategic initiatives, EMEA at Gracenote. “Based on our position at the heart of this ecosystem, we are uniquely positioned to help content publishers from small to large benefit from the maturation of FAST and we look forward to helping them achieve better FAST content visibility, consumption and monetization through our Program.”  

In announcing the launch, Gracenote highlighted the growing importance of FAST offerings in the streaming landscape and cited data from Statista projecting that revenue generated by FAST channels worldwide will reach $18 billion by 2028 with the U.S., U.K., South Korea, Germany, India, Canada and Brazil the leading markets.

To capitalize on that growth, Gracenote explained that content owners need high-quality normalized metadata and connected content IDs to facilitate distribution on streaming platforms, drive program and channel visibility as well as increase viewership.

Gracenote FAST Program participants that submit their channel portfolios directly to Gracenote benefit from expert enrichment of editorial and contextual data plus access to rich program imagery which drive increased channel engagement. Assignment of Gracenote IDs to participants’ content streamlines placement on major streaming platforms through Gracenote Listing Distribution Services and enables easier content discovery, the Nielsen unit said. 

In addition, Program participants receive a “Gracenote Certified” designation giving them an advantage when pitching their FAST offerings to platforms which prefer and in some cases require standardized Gracenote metadata and IDs. This label ensures that all data meets the various requirements for the streaming platforms resulting in a turnkey program for participating content providers.

Gracenote will exhibit at IBC in Amsterdam from 15 - 18 September. Company executives will also participate in a fireside chat titled “FAST Talk: A Discussion on Global Expansion Challenges and Opportunities” and deliver a presentation titled “Metadata: The Essential Building Block for FAST.”  To learn more, visit https://events.nielsen.com/ibc2023. 

VALNECIA, Spain—Yuvod, a provider of cloud-based OTT and IPTV solutions, today announced a new partnership with global media services provider Red Bee Media. 

Yuvod’s end-to-end, cloud-based streaming platform will be integrated with Red Bee's API services, positioning Red Bee Media as the main integrated supplier of EPG metadata to be included with Yuvod’s turnkey Platform-as-a-Service (PaaS) solution in the U.S. market, Yuvod said. The company plans to demonstrate the benefits of Red Bee, including their flexibility and exemplary customer service, as an integrated metadata supplier in preparation of launching Yuvod’s solution in the U.S. and Canada.

“Video providers big and small must deliver the same flawless, high-quality experience that can be easily accessed without costing a fortune,” said Ricardo Tárraga, co-founder and CEO at Yuvod. “By partnering with Red Bee, we can provide easily implemented access to advanced yet cost-effective streaming solutions that deliver an unbeatable viewing experience for end users, all entirely in the cloud.”

“We’re excited to partner with Yuvod as they expand their offerings in North America,” said Jason Marchese, Head of Sales, Market Area Americas at Red Bee Media. “By pre-integrating Red Bee’s rich metadata, Yuvod’s cloud-based model can provide video service providers the ability to scale their operations and adjust resources to meet the evolving demands of the market while still reducing costs.”

Yuvod’s holistic platform delivers live and on-demand streaming for TV programming and events, as well as time shift streaming, with advanced functionality across multiple devices and applications. It can be deployed in any location and centralizes all operational and technical processes into the streamer’s hands. Yuvod provides a proprietary video platform, media server, middleware, STB integrations, customizable dashboards, choice of content delivery networks (CDN), IP networking, multi-DRM encryption, CRM and billing systems, customizable app design, 24/7 support—available on Android mobile, Android TV, iOS, Smart TVs and more.

WHITE PLAINS, N.Y.—The Society of Motion Picture and Television Engineers (SMPTE) today published its initial release of the Rapid Industry Solutions (RIS) On-Set Virtual Production Camera and Lens Metadata project.

The goal of the overall initiative is achieving interoperability of all on-set equipment, and improving consistency of camera and lens metadata is an important step, SMPTE said.

“This release is the first of many free, open documents SMPTE will offer through the work of our RIS initiative,” said David Grindle, executive director of the society. “Through the combined effort of our RIS team, and the community as a whole, we are working to refine these new tools for storytelling.”

The initial release targets traditional VFX—a step toward future releases coving camera and lens metadata for on-set virtual production and camera tracking metadata. Hosted in a public repository for SMPTE members and the public to provide comments and feedback, the work includes reference code that converts the output of camera makers’ tools into a normalized JSON format, it said.

Additional releases are planned to advance towards the goal of covering camera and lens metadata for on-set virtual production and camera tracking.

“SMPTE RIS was able to bring together key experts from the major camera and lens makers with cinematographers and VFX experts to define the metadata parameters that are most important for downstream use in traditional VFX,” said Jim Helman, chair of SMPTE’s Camera and Lens Metadata group and CTO of MovieLabs.

“For years at MovieLabs, we’ve been hearing from our studio members about how important metadata from on-set doesn’t make it downstream in a usable and trusted form, resulting in wasted time in finding, reconstructing or back engineering that metadata. These metadata tables and code are an important step toward improving that situation and fit well with our own work on metadata and ontology in the MovieLabs 2030 Vision for media creation.”

David Hoffman, business development manager of the Americas at Blackmagic Design, co-chairs the RIS Interoperability Workstream along with Jim Geduldick and Ritchie Argue. The project director for RIS is Ryan Hendricks, the society said.

SMPTE RIS is also working toward several other deliverables for 2023. The Interactive Virtual Production Wall Chart will become available by the end of the year and will showcase how virtual production works from start to end. SMPTE RIS and the On-Set Virtual Production (OSVP) initiative were created to address technical challenges, it said.

More information is available on SMPTE RIS is available online.

More information on the Camera and Lens Metadata project is available here.

LOS ANGELES—The media and entertainment catalog management solution provider Fabric has acquired Internet Video Archive (IVA), which has a large inventory of entertainment trailers, as well as a substantial baseline catalog of film and television metadata.

Financial terms of the deal for IVA were not disclosed. 

For the past 18 years, IVA has been providing movie, TV, and game promotional content to some of the largest names in the media industry. IVA also provides insights on titles that are trending on a daily basis, up-to the minute rankings, future releases, and links to where to view or purchase content. 

The acquisition enables Fabric, whose clients include Warner Bros. Discovery, Paramount Global, HBO, MGM, and FOX, to instantly match titles to IVA’s library, and enable the sharing of data across Fabric customers.

"With mutual clients calling for IVA to integrate with Fabric, it was clear that our two services were highly complementary,” said Rhodes Mason, president of IVA. “Fabric's ability to connect supply chain systems as well as manage and enrich titles is a natural fit for IVA. The combination can enable both enrichment and data matching, as well the potential to update tens of millions of consumers across IVA’s content discovery platforms in an instant. This benefits IVA’s clients with real-time data at the point of creation and gives Fabric’s customers greater control of data provenance. The combination of the two companies will enable customers to efficiently capitalize on all the new and innovative entertainment products that exist today and into the future."

Fabric now securely manages over 20 million titles for its customers, enriching catalogs with thousands of attributes that help to better connect content with audiences around the world. It also provides native enrichment and supply chain automation processes that provide its customers with a combined savings of more than $10 million annually compared to tasks that have historically been managed manually.

“Integrating IVA’s services natively into the Fabric platform will help standardize our clients’ title catalogs more easily, provide insights about their marketing, and verify their licensing across streaming platforms,” said Rob Delf, CEO of Fabric. “At the end of the day, this acquisition is about listening to the needs of our customers, and coming up with ways to deliver more value through our tools and services.  This acquisition will only accelerate Fabric’s ability to manage customer title catalogs efficiently and cost-effectively in one place, and enable the sharing of that information with better control and accuracy.”

MOUNTAIN VIEW, Calif.—Based on an analysis of key measures and product usage from thousands of customers, TVU Network is reporting that some of the top trends among its customers include: the increased use of metadata-driven content throughout the media supply chain; the escalation of the FAST channel ecosystem; and the acceleration of 5G networks adoption for acquisition.

“Sports, entertainment, and news organizations throughout the world are discovering the power in delivering actionable data while on air,” said Paul Shen, CEO, TVU Networks. “Besides being able to make it easier to find relevant video content, the added information within metadata boosts the ROI of content. The shift toward data-driven technology for large media organizations will only grow as a means to support the entire organization.”

The value of video metadata and its ability to make it easier to find relevant clips gained  momentum in 2022, as evidenced by the increased use of the AI- and cloud-based TVU Search, the company said. 

Content creators helped propel a more than 630% increase in hours of video ingest in 2022 versus 2021, including live feeds from the year’s major news, entertainment and sporting events. With TVU Search, content creators can quickly find the clips they need for a story without having to scan through hours of video, resulting in significant time savings. 

TVU Search allows the user to quickly locate and clip live or historical content at any time from anywhere. It automatically adds metadata to the content at ingest, making identification of relevant content easier than ever before.

Another key trend was the addition of FAST (Free Ad-supported Streaming TV) channels, TVU said. According to a special report in Variety this year: “...the number of available FAST channels in the U.S. is over 1,400, with 22 major providers including Pluto TV, Tubi and Roku Channel.” According to nScreenMedia and Verizon Media, FAST platforms sold $2 billion in advertising in 2021, which will grow to more than $4 billion in 2023.

This is reflected in usage numbers for TVU Channel, a cloud-based playout service, the company reported. 

TVU Channel saw over 1000% increase, up 1217%, in on-air use this year over last. With an as-run log included in TVU Channel, it’s ideal for FAST channel playout or for any content creator looking to start a fully ad insertable channel. 

Another big driver for live video production and delivery this year was the availability and proven benefits of 5G networks, TVU reported. 

The numbers for TVU One 5G-enabled mobile transmitters bear that out. In January of 2022, 7% of the TVU One units shipped were 5G-equipped. In December of 2022, that percentage rose to 78%.

“The uptick in 5G implementation combined with the increase in remote production has created more opportunity for video producers around the world,” said Shen. “5G infrastructure, expanded network capacity and flexible configurations continue to drive explosive growth in live, immersive, and interactive experiences.”

In terms of global deployment, hundreds of TVU One 5G mobile transmitters and RPS Link Encoders were commissioned for the tournament in Qatar. Brazil’s leading TV network, Globo, employed 17 TVU One’s for its live coverage. With the exclusive rights to broadcast the event in Brazil, Globo produced over 300 hours of transmission with TVU One 5G units.

“This year was a significant shift in the way customers used our technology,” reported Shen. “We’ve seen a greater importance placed on mobile portability, metadata-embedded video that propels the entire production process and distribution to multiple platforms, it’s been a complete migration in the media supply chain. We’re gratified to see our customers derive such value from what we provide.”

FREMONT, Calif.—Digital Nirvana has added support for Avid CTMS APIs to its MetadataIQ software-as-a solution (SaaS) tool for automatically generating speech-to-text and video intelligence metadata, the company said today.

With the new Avid support, editors and producers using MetadataIQ can extract media directly from Avid Media Composer or Avid MediaCentral Cloud UX (MCCUS) rather than being required to connect first with Avid Interplay. As a result, broadcasters, post houses, sports organization and other Avid users are not required to have Interplay in their environments to use MetadataIQ, the company said.

The support gives all Avid Media Composer and MCCUS users the ability to insert speech-to-text and video intelligence metadata as markers within an Avid timeline, it said.

It also enables the ability to:

• Ingest different types of metadata, such as speech to text, facial recognition, OCR, logos and objects, each with customizable marker durations and color codes for easy identification of metadata type.
• Submit files without having to create low-res proxies or manually import metadata files into Avid Media Composer and MCCUX.
• Automatically submit media files to Digital Nirvana's transcription and caption service to receive the highest quality, human-curated output.
• Submit data from MCCUX into Digital Nirvana's Trance product to generate transcripts, captions and translations in-house and publish files in all industry-supported formats.

These capabilities will improve the workflow for media companies that use Avid Media Composer or MCCUX to produce content, it said.

"MetadataIQ used to only connect with Avid Interplay for media extraction, which meant customers had to use Interplay in their Avid environments to use MetadataIQ. With the success of MetadataIQ and its ability to enhance content production, Digital Nirvana… received a lot of requests from prospective customers to extend our services to non-Interplay users. And that's exactly what we've done," said Digital Nirvana CEO Hiren Hindocha.

"This integration makes it so editors can use MetadataIQ throughout the entire pipeline without having to create additional proxies, import metadata files or anything else — and no Interplay required," he said.

More information is available on the company’s website.

AMSTERDAM—InSync Technology has announced that it has entered into an OEM partnership agreement with metadata solutions provider, GrayMeta, to deliver a solution for high-quality content ingest with automated metadata creation. 

The integrated offering is particularly relevant to archiving applications. It has already been deployed by the Library of Congress, the world’s largest library and will be shown at IBC 2022 between Sept. 9 and 12 in Amsterdam. 

The solution draws on InSync’s heritage and expertise in delivering ultra high-quality content for broadcast applications, and GrayMeta’s deep knowledge of leveraging metadata to increase revenue. 

InSync’s AE2020 Analysis Engine will now form part of GrayMeta’s Emmy Award-winning System for Automated Migration of Media Assets (SAMMA), which performs video digitisation and supports migration of tape archives to file-based assets. The integrated solution is designed as an input stage to receive analogue and digital outputs from legacy storage technologies and optionally apply a raft of signal conditioning options.  

InSync Technology’s managing director, James Taylor explained that “We are proud to partner with an innovator such as GrayMeta as we meet the growing demand for unmatched quality and cost-effective conversion in archiving. The combination of InSync AE2020 and SAMMA creates an elegant and powerful solution that can handle a comprehensive range of formats.” 

Together, InSync and GreyMeta have delivered a solution that can apply a wide range of signal conditioning, including time base correction, RF dropout compensation, noise reduction, procamp, gamut legalization, and aspect ratio conversion. It also automates the process of managing the migration of videotapes to digital files. 

"We are delighted to bring InSync Technology’s analysis engine into our archiving ecosystem,” said Chi-Long Tsang, SVP & Principal Architect with GrayMeta. “We are now able to offer customers a comprehensive solution that makes archived content more accessible and reusable across a wide range of applications.”  

The integrated InSync and GreyMeta solution will be shown on stand 2.A18 during IBC. 

EMERYVILLE, Calif.—Nielsen's Gracenote has launched Gracenote ID Distribution System, a new platform that enables program owners and creators to leverage Gracenote IDs to manage their content, streamline distribution and ensure their content brands are well represented and easily discoverable on major streaming services, connected TV devices and MVPDs. 

The product is designed to help content owners make their entertainment offerings more successful and improve return on investments, Gracenote said. 

“With streaming reaching maturity, more focus than ever is placed on securing positive return on investment on programming,” said Simon Adams, chief product officer at Gracenote. “To achieve this, content sources need their offerings to be easily discovered and appropriately represented in distribution channels and their user interfaces. Gracenote ID Distribution System leverages our deep metadata and content ID expertise to streamline this process for content sources, ultimately helping them drive maximum engagement and monetization.”

At launch, Gracenote ID Distribution System is available in North America, Europe and Latin America.

Gracenote IDs are unique identifiers for TV shows and movies that are broadly deployed throughout the global entertainment ecosystem and act like Universal Product Codes for content. 

hese IDs link data assets related to programs such as descriptions, genres, cast lists, vibrant imagery, and program availability on a platform. In addition, they power personalized recommendations within individual video services and universal content search and discovery across streaming, MVPD, CTV, on-demand and linear platforms, the company said. 

Using Gracenote ID Distribution System, programming sources can submit their content metadata and imagery via a web application or API to ensure its association with normalized Gracenote metadata and IDs and manage its distribution, all through a single point of integration, the company said. 

The majority of the world’s leading video discovery experiences running on Gracenote metadata and IDs can then present resonant content and metadata in program guides and recommendations to drive viewing and engagement.

The company said that key benefits include: 

• Greater visibility and ownership: Powerful tools help users better manage their metadata and understand its distribution on downstream platforms. They can obtain Gracenote IDs earlier in the content lifecycle enabling them to track programming at every subsequent stage.
• Optimized discovery: Users can ensure optimal availability of their programming in Gracenote datasets elevating discoverability. Upon request, first-party metadata variations can be delivered to specified video discovery platforms to maximize regional relevance or satisfy branding requirements.
• Streamlined distribution: The platform enables programming sources to efficiently secure broad distribution across Gracenote’s global customer base and engage their end users.

Configuring an end-to-end system storage solution for a new facility can be challenging. Systems once built upon the theory that “storage tiers” were key dividing elements—today, the challenges include managing data movement between such tiers and tailoring the storage types for best uses and applications.

Storage tier assignment was a model that separated “continuously-active” storage from “not-so-active” storage, i.e., fast vs. slow or bulk storage. Legacy storage tier designs continually pushed data amongst storage tiers based upon the associated activities known at the time of the design. As storage capacity requirements increased, more drives were “thrown” at the system.  Dissimilar drive capacities created differentials with performance measurement becoming unpredictive.  

As “cloud” and other sophisticated storage architectures came into play, a different science emerged. Previously dedicated “hierarchical architectures” changed functionality to leverage software solutions which manage needs, capacities, performance, and timing. By carefully modeling with best-of-breed data storage products, new solutions could be solved using complex data activity predictions and automation. No longer is it practical to use a single storage element or just the cloud, alone, to support modern media production activities.

Managing by Type to Fit the Need
Highly sophisticated structures in today’s media asset management solutions allow storage administrators to apply variants in storage sets across groups of individual topologies and workflows (Fig. 1). Software-driven approaches coupled with fast flash memory, and other backbones (NVMe, PCIe) now allow the administrator to select workflow segments and marry them into independent systems appropriately configured for specific workflows across the enterprise.

Add in a growing proportion of media technology which is moving to the cloud, and hybridized choices open up wider possibilities amongst on-premises and cloud workflows.  

Cloud supports ingest operations to funnel content from many geographic locations. Depending upon the need, placing accumulating data into a cloud platform offers a lot of extras, including long-term storage of content in its raw form plus the ability to consolidate content into a single virtualized allocation.

Knowing and defining the variety of steps to produce airable content is essential to determining if data is stored on-prem, in the cloud, or both. When and why to place which data in one or more locations vs. the efficiency or performance perspectives for specific or immediate workflows is what defines “managing by type and fitting the need.”

Differing Storage Structures and Elements
Organizations with varying requirements may need immediate, fast access to content at speeds incompatible with the capabilities of a “cloud-only” architecture; some workflows may not need the additional features, functionality or capabilities offered in a cloud solution. 

For media organizations, a workflow might simply push “raw” content straight into editorial production. When facing a breaking or live news requirement, they may need to push the new content straight to playout while other activities (including editorial) occur in parallel.

Often workflows must support multiple parallel processes, repetitive alteration, and documentary-like preparation for later specials or features.  In “live” production, raw content often needs at the very least a “technical preview” before being aired “live.” Producer approval or other reviews may also be required prior to pushing straight to air.  

Proxy generation, while simultaneously moving the data from an ingest cache to playout services, could gain efficiency from differing storage platforms. While some may only need a “tops-and-tails” segmentation, others may only require a mid-sequence removal or audio overtone.

These “fast vs. slow” example workflows demand the ability to quickly take in content, transform it to an “airable” format, and assemble it for purpose necessitates fast delivery process versus lengthier or more conventional tagging, shifting into editorial cache, or moving into near term storage for other production purposes.

Attention to Metadata
Tagging or metadata association for any number of workflows or production/legal mandates are not uncommon. Metadata collection covers activities from automated tagging to scene-change detection to detailed content analysis or evaluation with respect to people, location or purpose.  These variations gain additional benefits by their placement onto different types of storage depending upon the degree of effort required for each workflow.  

Such differentiation by activity helps reduce load balancing and may mitigate “bottlenecking,” which drags down other processes involving multiple read/writes or transfers between processing platforms. Other methodologies, such as Kubernetes and microservices structures, are leveraging AI principles to improve performance and speed up operations.

Depending upon the workflow, data may be repositioned to fast disk arrays based on the best system approach using the available targets. Storage that can materialize the information into a particular format, managed by a MAM, and sent to the cloud or straight to air ahead of editorial may also be sought. Thus, we recognize that “not all storage is created equal.” 

Monitor Proactively
High-availability storage systems need to be “watched” to ensure bottlenecks, over-provisioning, or other random events do not occur. Administrators who manage storage sets, manipulate the best data paths, and leverage file-based workflow activities proactively keep an eye on storage volumes, overall system bandwidth, and the management of those processes.  

Some less intense or non-immediate activities take more time to complete their operations and would best be allocated to hours when other commands needing higher performance are more immediately addressed. For example, if the storage volumes associated with editing command more reads and writes than simple reads for transfers—move those less important activities to overnight periods when less editing occurs. 

Administrators routinely must watch the logs to ensure that up-time, system loading, and security are handled proactively. 

Suggestions include scheduling upgrades at times when high-availability periods are unnecessary. Establish a hierarchy of notifications and reporting to ascertain when peak performance periods need full accessibility. High-availability work-periods, if monitored and reported proactively, can support most maintenance without downtime, and can occur during a flexible and convenient time based upon a routine schedule.

Extreme Hyper-scalability
Selecting the appropriate storage components requires understanding the entire media ecosystem. Workflows vary, so storage system administrators must engage components which are sufficient in scale to handle the average workflows yet still provide for those “hyper-activities” whereby the next level of availability can be enabled as necessary. Flexibility and capacity overhead for burst-up periods need consideration throughout the various workflows.

Speed and throughput are usually expected during “peak periods” but not necessarily at all times.  When a workflow pushes the envelope or approaches the edge of your storage infrastructure’s routine capabilities, your storage architecture should be expected to “spin up” to a higher level to address short-term needs. Changing when those activities occur (i.e., to “off hours”) may alleviate the potential for bottlenecks during peak periods.  

Some vendors can autonomously elevate their system performance by employing “capacity module” extensions. Additional support is usually configured using flash storage in an NVMe over PCIe architecture. These modules need not be deployed throughout all storage sub-systems; and are likely relegated only to your “highest-performance-level” activities. Plan for potential needs in massive scalability, also known as “scale out and scale up”—this extra horsepower may be necessary for one-offs or major events (e.g., Super Bowl, Final Four or breaking news). 

We’ve only touched the surface of modern intelligent storage management and its details. When considering an upgrade or new storage platform for a current or greenfield facility, be sure to take notice of the competitive and continual changes occurring in the storage-for-media sectors. You may be surprised how the many new on-prem storage additions are changing those capabilities which are essential to media productions. 

WHITE PLAINS, N.Y.—The Society of Motion Picture and Television Engineers together with MESA, a data IT and security specialist community devoted to the future of the M&E industry, is placing the human- and machine-readable Language Metadata Table (LMT) into the SMPTE pubic review process, the society said. 

The process, an early step towards SMPTE standardization, involves publishing LMT as a SMPTE Public Committee Draft (CD), which is a vetted and approved list of language codes that are available for public comment, implementation and validation, it said.

"The Language Metadata Table was started at WarnerMedia in 2017 to normalize language codes within the organization, and IETF BCP 47 was selected due to its flexibility," said Yonah Levenson, LMT chair at MESA. "As interest in LMT as the M&E industry's language code solution increased, SMPTE recognized the value of the LMT and came on board as the technical partner/advisor."

LMT offers media companies and related enterprises a controlled vocabulary and standardized set of codes for accurately identifying spoken and written language, which is intended to enable more efficient media interchange around the world, SMPTE said.

It supports many applications, including audio, written and timed text, such as closed captions and subtitles, accessibility, licensing, content localization and international distribution.

SMPTE Technology Committees (TCs) have begun working on the LMT register. They are expected to produce a SMPTE Public CD in the first half of 2021. The Public CD process allows the society to put the LMT register into the public domain quickly and begin gathering feedback and making improvements to both the register and guidelines for its independent management by multiple stakeholders.

"If you buy and sell media, you understand that a common vocabulary for language tagging is sorely needed," said SMPTE Standards Vice President Bruce Devlin, who worked with MESA to develop the prototype LMT register and online interface currently being reviewed by SMPTE TCs.

"The LMT register accounts for all languages as well as dialects and scripts. As we see the register through the Public CD process, our hope is that the LMT register will become a canonical resource that serves the needs of all media organizations and ecosystems. Accessing this data will be as simple as clicking on a link or using an API to grab required codes," said Devlin.

SMPTE TCs are reviewing the prototype LMT register to determine if the structure of the dictionary is correct and if the process for updating that dictionary is correct. After this step is complete, the dictionary and update process will enter a public review period, during which people and organizations can try out the register and use a dedicated GitHub repository at github.com/smpte to provide real-world feedback that will inform iterative improvement of the register. The society will leverage the SMPTE Knowledge Network, which is built on a flexible Microsoft Teams environment with integrated apps including the Microsoft 365 suite and GitHub, to bring agility and efficiency to the Public CD process.

Now up to 239 languages, the LMT register can be found at www.mesaonline.org/language-metadata-table.

More information on MESA is available on its website.

More information on SMPTE is available online.

LOS ANGELES—The Society of Motion Picture and Television Engineers (SMPTE), the leading standards governing body for media & entertainment engineering, announced it has joined with the Media & Entertainment Services Alliance (MESA), a leading community of media technology executives, to co-develop the Language Metadata Table (LMT) standard. 

This is the first project that SMPTE has undertaken to electronically publish machine-readable metadata as a standard that is expected to be used operationally rather than by application developers. It is expected to significantly streamline media asset management around the globe, the organization said.

Conceived by WarnerMedia, MESA supports the LMT Working Group as one of its communities of practice. LMT, based on IETF BCP 47 (Internet Engineering Task Force Best Common Practices) guidelines, is the first list of language codes vetted and approved as an industry standard for media and entertainment. It greatly benefits media companies, content owners, video service providers and others who need standardized distinctions between spoken and written language and consistent codes. Other international standards bodies have created controlled vocabularies; however, they are broadly used for many purposes. The constraints of the LMT provide a controlled vocabulary for what is needed in the professional media industry.

LMT codes will be used for a plethora of content-oriented applications, such as audio, written and timed text like closed captions and subtitles, accessibility for the visually and hearing impaired, licensing and localizing international content and distributing non-English content.

“Having an authorized system of consistent codes reduces the Tower of Babel and simplifies development work by enabling all parties to work off the same table,” said Yonah Levenson, manager, Metadata Strategy and Terminology Governance at WarnerMedia and founder for LMT. “SMPTE’s publication of LMT will help everyone manage their media assets by removing cumbersome processes like maintaining mapping tables and having different payloads for different distributors.”

The MESA-organized LMT work group committee of data experts are helping to share and expand the standards table, and have created universally accepted language terms, definitions, examples and templates for more than 200 language codes and display values, with more in development. These members include HBO, Turner, Warner Bros, Disney, WWE, NBCUniversal, Paramount, Sony Pictures, Showtime, Lionsgate, Fox and other organizations.

“We have long regarded the LMT as a critical tool for the future of media and are excited to see it join the ranks of SMPTE’s foremost industry standards,” said Mary Yurkovic, Smart Content Community Builder at MESA. “This brings together the hard work of MESA members, standards groups and top metadata experts, who are tackling all the industry’s nomenclature one classification at a time.”

LMT will be published in SMPTE’s Registration Authority (smpte-ra.org), a highly sanctioned and publicly accessible database with registers of metadata elements, unique identifiers, values and more for the media and entertainment industry.

To ensure the LMT registry keeps evolving, SMPTE will use its public committee draft (public CD) process, which provides open documents that are distributed for review and commented on by industry professionals prior to completion of the full standardization due-process. The LMT programming tools will also be available as opensource code to developers worldwide, and as with any registry SMPTE maintains, the LMT entry will facilitate interoperability.

“SMPTE’s work on LMT is a crucial step in addressing the broad challenges and complexities around sharing data and content in the media and entertainment industry. We are grateful to MESA for choosing SMPTE as a home for this standard,” said Bruce Devlin, vice president of SMPTE standards. “Anyone involved in international distribution will greatly improve their media asset management workflows thanks to the new standardized language codes this introduces.”

Aided by MESA, the work group will continue within the rules of SMPTE’s due process to further define and document its policies and procedures, update additional languages and partner with organizations including EIDR, ISDCF, and MovieLabs to strengthen LMT and support broader adoption.

For more information, please visit https://www.mesalliance.org/language-metadata-table.

FREMONT, Calif.—Digital Nirvana has pulled the curtain back on its new cloud-based closed-captioning and metadata enhancement system, Trance. Meant to provide new efficiencies for captioning and subtitling workflows, Trance offers features that include a cloud-based infrastructure, an integrated and automated speech-to-text engine and an easy-to-use interface.

Integrated with a proven STT engine, Trance has advanced speech-to-text capabilities for generating automatic, multilingual transcripts for every work item within the application.

The platform also centralizes workflows such as job allocation, caption edits, role assignment, report generation and status monitoring for streamlined project management. The cloud-based architecture allows for the access of content from anywhere as well as eliminating the need for individual software installs and updates. In addition, Trance can ingest Avid assets directly for caption generation and/or metadata enhancement.

Users have the ability to access media assets from cloud storage, Avid Media Central, SFTP, local servers or through an API, with the final output able to be automatically converted into multiple sidecar formats and sent to predefined destinations.

Digital Nirvana is expected to feature the Trance system at its NAB Show New York booth, N369.

Having more or less mastered the process of converting media to IP, broadcasters now can focus on the opportunities that arise as content is placed into a multilayer IP framework. Intelligence and power across this network open the door to greater innovation and the introduction of compelling new services. Of course, with great power and great intelligence come some challenges too.

With the shift to IP, all elements of the network—from endpoints to the core of the network—have become intelligent and addressable. Cameras, receivers, displays, software—every element has a name that can be used to identify and refer to it, classify its capabilities and allow it to be authenticated so that it can use services in the network.

Broadcasters working in the IP realm therefore need to establish robust management layers that not only control intelligent elements across the network, but also apply some rules with regard to the movement of content and data. To ensure all of these exchanges take place as they should, broadcasters also need to be able to monitor systems and data across the network.

THE DIFFERENCE FROM SDI

When dealing with content in the SDI world, broadcasters enjoyed fairly straightforward monitoring. The signal was contained in a cable, and it stayed in there. Now, though, as soon as content gets into the network, it hits the switch along with numerous other flows. Although they are routed as independent IP traffic, different flows can very easily get mixed if problems occur along the network. Such problems are often difficult to trace, and broadcasters need specific monitoring tools in order to pinpoint problems associated with IP networking or other elements.

Along with media flows being carried over the network, there are timing flows, as well, with synchronization using PTP being carried through. These timing flows require tools that tap into the flow and extract information that confirms the health of the flow. Broadcasters must address these types of management and monitoring challenges across the whole framework, using highly intelligent layers to assess the well-being of the network.

Authentication and security are critical concerns as IP-based cameras, sources and receivers are deployed in large quantities. To reduce their vulnerability to distributed denial of service attacks (DDOS) and similar malicious acts, broadcasters will need to create “walled gardens” and implement safeguards to protect from these and from other rogue entities.

With these challenges in hand, broadcasters can take advantage of a powerful multilayer IP framework to explore new opportunities. In terms of facilities, broadcasters can move away from purpose-built infrastructure and thereby reduce cost while taking advantage of developments coming from the larger network community. In terms of staffing and operations, the ability to control content (audio and video) and data flows independently allows broadcasters to realize greater operational efficiencies. Depending on the needs of the application, they can send audio, video and metadata to the facility and people who can most cost-effectively process that content.

THE POWER OF METADATA

In production and broadcasting, IP allows for more extensive analysis and richer viewing options. For a high-profile sports broadcast being shown around the world, IP makes it easy to replace a French- or English-language audio flow with a Greek, Spanish or Japanese flow. Metadata from the broadcast can be leveraged to support in game-analysis, as well as archiving and searches within just minutes of the event.

Just name the sport, and data is being used to help talent convey the strategy behind player choices and the skills that are required of players, whether it’s a golfer’s club head speed or launch angle on the golf ball or the revolutions on a baseball thrown or a soccer ball kicked. Consumers’ thirst for that kind of information will never be quenched, and IP makes it easier for broadcasters to offer more data and better analysis.

IP also supports greater viewer autonomy in selecting the feeds or angles they get to watch. Right now the technical director calls the shots, but in the long term, broadcasters will take advantage of the IP framework to give viewers the ability to select shots of interest. Looking even further ahead, adoption of artificial intelligence will open up amazing capabilities in terms of archiving and recalling specific content.

These are just a few of the significant ways in which content delivery and consumption will evolve as the broadcast industry embraces an IP-based framework. Obviously, it’s not a done deal, but vendors continue to work on solutions that address concerns across monitoring, security, service assurance and availability. The opportunities vastly outweigh the nontrivial challenges the industry faces in migrating to IP. And, when the whole IP framework has been put in place, the possibilities will be endless.

Rafael Fonseca is vice president and director, product management for Artel. 

MOUNTAIN VIEW, CA – April 17, 2019 – TVU Networks, the global technology and innovation leader in live IP solutions, today announced industry-wide recognition of the new 4K version of its TVU One mobile IP transmitter, the TVU MediaMind Appliance AI-based metadata generation and search engine, and its IS+ proprietary transmission algorithm during last week’s 2019 NAB Show held in Las Vegas. TVU also provided mobile cellular transmitter equipment to producers of “NAB Show LIVE,” the official broadcast of the NAB. The live transmissions went flawlessly, and Broadcast Beat, producers of “NAB Show LIVE”, will continue to use TVU equipment at future events and venues.

“We’re thrilled to have these different organizations recognize different sectors of our work,” said Eric Chang, Vice President of Marketing, TVU Networks. “This really points to the depth of our company and to the IP-based solutions we provide to a range of applications occurring in diverse locations. The products and technologies selected are all next-generation improvements and essential in the creation of a story-centric workflow. We’re focused on pushing the limits of what’s possible to meet our customers’ live content production challenges today and in the future. I believe our run of award wins during the past 10 years as an NAB exhibitor is a direct reflection of our continued commitment to live production innovation.”

In the first NAB Show 2019 Product of the Year Awards, TVU Networks won in the AI/Machine Learning category for its TVU MediaMind Appliance - an AI-based metadata generation and search engine that speeds production performance and content reuse. This is an independently judged awards program that recognizes the most significant and promising new products and technologies exhibited during NAB.

TVU MediaMind, is a metadata generation and search engine that speeds production performance and content reuse within an existing broadcast media supply chain. The system uses a combination of time-code and AI-based facial and speech recognition to generate metadata. And an open API lets users connect directly with MAM and NLE systems. Because the MediaMind appliance utilizes both AI and time code to generate search results, it can convert audio and video into structured data in real-time. It’s capable of searching a raw asset at any stage of production – from assignment through to on-air. With the help of TVU’s AI engine, real-time speech recognition lets users find text results even faster than they would searching for images alone. Users simply type in what they’re looking for and the appropriate section of a time code appears. The real-time content search engine uses time code to identify each asset with a unique ID.

TV Technology magazine presented TVU Networks with a Best of Show Award for the TVU One 4K cellular mobile transmitter during the 2019 NAB Show. The awards are judged by a panel of industry experts on the criteria of innovation, feature set, cost efficiency and performance in serving the industry. All nominated products are featured in a special Best of Show Awards Program Guide, which will be distributed in digital edition form to more than 100,000 readers of seven Future publications.

Also introduced during the 2019 NAB Show, the 4K version of the TVU One mobile transmitter, a cellular mobile unit with the ability to transmit Ultra-HD/4Kp60 HEVC content. The TVU One TM1000 is TVU’s flag-ship HEVC cellular 4G/LTE mobile transmitter. “UHD/4Kp60 is a much more technically challenging resolution to achieve in a cellular mobile unit,” explained Chang. “However, we are able to overcome these challenges and encode video in full UHD/4K at 60 frames per second.”

The TVU One offers a robust set of functions to meet the needs of any live video broadcaster. It delivers industry-leading video transmission resiliency and HD picture quality from an ultra-compact and rugged hardware chassis. With TVU One, users can fully leverage the versatility of a lightweight IP-based video transmitter in the field as a part of an end-to-end workflow without sacrificing performance, features, or picture quality.

PRODU presented TVU Networks with a NAB 2019 Tech Award for its IS+ technology. TVU’s proprietary transmission algorithm, IS+ enables users to simultaneously transmit over multiple mediums with sub-second latency. Thanks to IS+, TVU transmitters can simultaneously aggregate any combination of bandwidth mediums including cellular 3G/4G LTE, WiFi, Ethernet, Ka-band and Ku-band satellite, microwave, microwave mesh and BGAN to deliver a reliable and dependable broadcast picture with latency as low as 0.5 seconds. IS+ uses advanced Forward Error Correction technology to produce superior transmission performance. When compared to traditional FEC/ARQ methods, IS+ uses minimal data overhead without latency introduced by packet retransmission. The result is a reliable transmission with high-quality images and ultra-low latency even in a moving vehicle.

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About TVU Networks®
TVU Networks® is the global technology and innovation leader in IP-based live video solutions. With over 2,500 customers in more than 85 countries, TVU Networks family of IP transmission and live production solutions gives broadcasters and organizations a powerful and reliable workflow to distribute live video content to broadcast, online and mobile platforms. The TVU Networks suite of solutions has been used to acquire, transmit, produce, manage and distribute professional-quality live IP HD footage as an integral part of news, sports and major global events. For more information about TVU Networks solutions, please visit www.tvunetworks.com

MOUNTAIN VIEW, CA – March 26, 2019 – At NAB 2019 TVU Networks, the global technology and innovation leader in live IP solutions, will announce the new MediaMind Appliance, a metadata generation and search engine that speeds production performance and content reuse within an existing broadcast media supply chain. The system uses a combination of time-code and AI-based facial and speech recognition to generate metadata. And an open API lets users connect directly with MAM and NLE systems. TVU Networks will exhibit in booth #C1707 during the NAB Show in Las Vegas next month.

“TV stations and news agencies have considerable media assets, but how can they be considered assets if they can’t be reused and repurposed?” said Paul Shen, CEO, TVU Networks. “While there are many ways to measure the value of content in the media industry, there are ultimately only two: who gets to the audience first and what content is viewed and reused the most? The faster the content spreads, the more it is reused and repurposed, the greater the value of the content and the lower the production cost. That’s what’s driven the creation of the TVU MediaMind appliance and the entire MediaMind workflow.”

Because the MediaMind appliance utilizes both AI and time code to generate search results, it can convert audio and video into structured data in real-time. It’s capable of searching a raw asset at any stage of production – from assignment through to on-air. With the help of TVU’s AI engine, real-time speech recognition lets users find text results even faster than they would searching for images alone. Users simply type in what they’re looking for and the appropriate section of a time code appears. The real-time content search engine uses time code to identify each asset with a unique ID.

Clips can be marked and saved for immediate use. There’s no need to wait for pre-production to finish or for the video file to be entered into the MAM system. With the reporter still on scene, the clip is ready to go online or on-air.

The TVU MediaMind appliance is part of a TVU SaaS model that uses the TVU MediaMind server (dual-channel VS3500/VS3100 models) as the edge server. The servers can receive video signals from any TVU Networks’ transmission device as well as process IP streams of standard transmission protocol, SDI video, and store files in any MAM system. They are professional video hardware decoders designed for the centralized viewing, control and distribution of live video content.

In addition to the MediaMind appliance, the MediaMind ecosystem includes the following cloud-based products: TVU Producer for multi-camera live video production; TVU Remote Production System (RPS) for remote production using a broadcaster’s existing studio control room and a public Internet connection from the field; TVU CAS (Contribution Automation Solution) for the identification of all video acquisition resources on a given story at acquisition; TVU Grid - an IP-based video switching, routing and distribution solution; and TVU Grid Market, a central source for content owners to contribute and pull video enabling assets to be shared and sold on a global scale. 

SAN JOSE, Calif.—Quantum has upgraded its Xcellis appliances powered by the company’s StorNext file system.

The new appliances are based on a next-generation server architecture that includes dual 8-core Intel Xeon CPUs, 64 GB memory, SSD boot drives, and dual 100Gb Ethernet or 32 Gb Fibre Channel ports. The significantly enhanced CPU and 50 percent increase in RAM over the previous generation greatly improve StorNext metadata performance, according to the company, making file auditing less time intensive, provide support for an even greater number of clients per node, and enable the management of billions of files per node. Customers operating in a dynamic application environment on storage nodes will also see performance improvements, the company said.

Quantum says that the 100 Gb Ethernet and 32 Gb Fibre Channel connectivity option allows customers to utilize the latest generation networking infrastructure between their clients and storage arrays, thereby accelerating workflows and improving productivity. Combined with StorNext’s capability to provide the most comprehensive and coordinated file accessibility over NAS, SAN, and DLC architectures, the new platform offers industry-leading architectural flexibility, Quantum said.

The new version of Xcellis gives customers the ability to provide cross-protocol locking for shared files across SAN, NFS, and SMB, optimizing performance for collaborative workflows and the ability to share content across both Fibre Channel and Ethernet. This will allow StorNext to provide even higher levels of streaming performance for video playback, with even greater reliability, according to Quantum.

Xcellis appliances provide a high-performance gateway for StorNext advanced data management software to integrate tiers of massively scalable on-premise and cloud-based storage. This end-to-end capability provides a cost-effective solution to retain massive amounts of data according to Quantum. In addition, StorNext offers a variety of important features that ensure data-protection of valuable content over its entire life-cycle. Customers can easily copy files to off-site tiers and take advantage of versioning to roll back to an earlier point in time (prior to a malware attack, for example) as well as set up automated replication for disaster recovery purposes, all designed to protect valuable digital assets.

Quantum’s latest Xcellis appliances are available now.

Ooyala has announced a partnership with the Digital Production Partnership to advance global news industry standards for the use of metadata.

The DPP’s Metadata Exchange for News is designed to establish standards for delivering news content, from planning and production to distribution and archive.

The initiative allows metadata generated in newsroom planning systems to be transferred to cameras and ingested into MAM systems, before being delivered to a publishing system.

This allows organizations to get breaking news stories to air quickly and to manage rights issues easily, as news packages and clips are clearly marked with copyright and restriction information.

DPP managing director Mark Harrison said: “News organizations want journalists and correspondents to be focused on the story, not the process.”

“With the participation of leading companies, like Ooyala, in the MAM and workflow-automation fields, we will advance more quickly toward these important objectives.”

Virtualization technology has already arrived in the broad world of smart business where automation and predictability hold the key to monetization. Industries such as telcos, manufacturing, energy, financial services and retail, among others, successfully adopted the cloud some time ago. However, when you look at the broadcast, media and entertainment sector and its vendors, it’s somewhat behind the curve. As a broadcaster, if you aren’t considering or testing cloud technology at this moment in time when other sectors have already made lucrative gains, then your business model is at a high risk of failure.

Long-established vendors in the world of broadcast, while still having a role in specialist functionality, are now joined—and arguably superseded—by a new influx of service providers. On the one hand, there are cloud companies like AWS, Azure and Google; on the other enterprise business management services like Salesforce and SAP.

MOVING FROM CAPEX TO OPEX

Over the last few years, these companies have broadened their feature sets to the point where the line on which one product finishes and another starts, has blurred beyond recognition. These "new" players are introducing completely new ideas when it comes to managing and delivering content, based on processing, connectivity and storage, while at the same time enabling the move towards new business models.

As one of the first companies in the U.K. M&E space to adopt a ‘cloud-first’ strategy and the first AWS certified M&E partner in the U.K., Support Partners are well versed in utilizing these disruptive technologies. As a company, our goal is to help all of our clients migrate parts or all of their infrastructure to the cloud by 2020.

Making the move from CapEx to OpEx is potentially transformative for the business models of content creators and deliverers. The OpEx model allows a direct link between costs and revenues, dynamically scaling on demand to capture those new opportunities. That, in turn, allows you to understand where profitability lies, and therefore concentrate on the more commercially compelling services.

Cloud technology is inherently agile. You can add new services, whether that is 4K Ultra HD production or machine learning for automated logging, simply by spinning up the appropriate microservices in the cloud.

A software-defined network allows you to move to a business-driven operation. Instead of someone in marketing asking for a channel and someone in engineering saying it will take six months to get it online, executives working in enterprise management software like Salesforce will be able to run a what if? exercise and immediately see how financially and operationally viable a new service would be.

[Read: It’s A Very Cloudy Forecast For 2021]

However, for most organizations it is daunting to make the jump to being cloud first, so where is the sweet spot between on-premises hardware and the cloud? Do you hand everything over to AWS, Azure or Google? Or do you use the cloud for its elasticity, stepping in when your on-premise hardware is at full stretch? And, if the latter, how do you know just how much hardware you should have on site so that it will be running close to capacity most of the time?

UNDERSTANDING SCALE

In a traditional broadcast architecture, the experienced system designer would look at a requirement and have a basic understanding of the scale. An installation might have, say, around 400 device ports, so to be safe the architect would specify a 512 x 512 router. Having set the size, the procurement people could talk to a selection of vendors and get the right deal on the right device. This CapEx model means that you can’t easily scale up without more investment or indeed scale down as and when required, plus you are tied to specific vendors.

In a virtualized environment the scale is equally vital when provisioning a system but it is far less readily understood. How many processor cycles do you need to render a 4K animated title sequence? How much storage do you need to edit a 13-part drama? How much additional processing power can you afford to call on in the cloud before you have to say a process will have to wait—and who makes that decision?

The answer: you need to understand the data. Not the content and its metadata, but the usage data. How much storage are you using and for how long? How many processors do you need, how many are in continual use and how many do you call on when a lot is happening?

Now the chances are you already have some cloud contracts or at least are negotiating at this very moment. The IABM/Devoncroft research published earlier this year said that 85 percent of broadcasters think they will be using the cloud in the next two to three years and 28 percent are already using it.

If you already have a cloud contract, did you get a good deal? It can be hard to tell when file transfers are billed by the gigabyte, processing by the minute, and asset management by the terabyte of storage.

This is a multi-dimensional matrix. The cloud provider will have a set of fees, which may include some hefty surcharges if you go over a certain level. The software provider will also have its own licensing model, which will be based on different parameters such as usage, nodes activated, codecs used etc.

The people providing media-specific software used to sell broadcast hardware. They have had to re-engineer their own businesses from a cash flow position where they received 100% of the value of a sale at the point of acceptance, to one where they are maybe hoping to get a recurring license of 20 percent of the value each year for five years. Their natural instincts are to ensure their return by nudging the license fee up; you may find yourself paying 25% a year, with the expectation that you will still be there five years from now. So, how can this be avoided?

AT A GLANCE

The solution is to have a means of monitoring and evaluating all the data and presenting it on a single pane of glass. If you were making something physical – a car, say – then you would want to be able to see the complete supply chain from component to customer on a single dashboard. Why should it be any different for the manufacture of intellectual property, like a television program or a commercial?

The solution surely lies in an integration between the broadcast and management infrastructures. Platforms like Salesforce have the dashboard functionality: you need to integrate it with the media technology to monitor trends, track usage and provide actionable insights.

Taking this approach builds an accurate, trusted dataset, derived from all available sources. Usage is reconciled between on-premise hardware and software services and cloud providers. That, in turn, gives you a real insight into how your transition to virtualisation and the cloud is performing.

More importantly, it allows you to apply predictive data analytics to do the what ifs? for your future plans. If you know accurately what you need in terms of processing, connectivity and storage today, you are in a strong position to make decisions for the future.

You know if you have a good deal today (or not), and you know what you want to achieve when you renegotiate. Armed with predictive data requirements, you are in a much stronger position to compare bids and costs. You are no longer locked into your current vendor for fear of getting a worse deal. You can use predictive analytics to understand how best to apply automation and machine learning, which microservices to spin up, and determine which spot instances are the best value at any particular time.

There is no doubt that the future for media is all about predictive automated workflows, running on software-defined architectures in virtualised environments, on-premise and in the cloud. Provisioning these systems calls for new skill sets and a new way of thinking.

At Support Partners, we have implemented predictive data analytics for one of the world’s biggest production and broadcast companies, but it is applicable to any media company. If you are not capturing your operational data today, you will not know where you are tomorrow. That will be the difference between commercial stability and the risk of failure.

Harry Grinling is the CEO of Support Partners.

WATERLOO, ONT.—Dejero has announced the addition of new metadata generation, device sharing and workflow automation features to its Dejero Control cloud-based management system.

Dejero has added a new automated, customizable recorded clip metadata generator to enable personnel in the field or a station’s control room to add and edit metadata to clips using the new Dejero Control interface, the company said.

Administrators at stations also can create custom metadata templates that can be used for all clips to add relevant, automatically generated metadata, making it faster and easier to search and find desired content. Users can select from 10 pre-set fields in the metadata template to add geolocation, acquisition date and other relevant metadata. They can also add their own fields to organize recorded clips, it said.

“With our new metadata feature, we are providing a means for broadcasters and media organizations to automate the generation and tagging of metadata, saving them valuable time and enabling them to speed up content retrieval and automate metadata driven workflows,” said Dejero CTO Todd Schneider.

Dejero has also built upon its existing device sharing capabilities that allow broadcasters to reroute a feed from a transmitter in the field to a partner station. Now broadcasters can also share access to receivers, offering stations greater flexibility in how they integrate with other stations and freelancers in the field. Sending and receiving video with the transmitters and receivers is now defined with the device sharing feature in Dejero Control

The company added that when these new features are combined with existing Dejero Control features, such as real-time analytics, usage reports and notifications, remote-controlled settings and transmitter health monitoring, stations can transform existing workflows to save time and allow their personnel to focus on the work that matters.

More information is available on the Dejero website.

TVU Networks, the global technology and innovation leader in live IP solutions, today announced the company showcased several breakthrough technologies for news production during NAB New York. Highlights in the booth included: TVU Producer for cloud-based multi-camera video production; newly established integration between TVU Networks and major media asset management (MAM) providers; and the TVU MediaMind set of solutions for AI-based automated video content management.

With TVU Producer, video content can be cost-effectively produced for different platforms or even audience groups completely in the cloud. Non-professionals can easily manipulate graphics, scoring and text overlays during production, thanks to its WYSIWYG interface. TVU Producer can handle multi-camera production from virtually any location. It supports simultaneously output directly to major social media platforms and virtually any CDN platform. SDI delivery is supported through a TVU transceiver. It also offers a multi-channel IP video switcher and slow-motion replay. 

The TVU Contribution Automation Solution (CAS), TVU AI Engine, TVU Real Time Search Engine, and TVU Workflow Engine comprise the TVU MediaMind platform. MediaMind simplifies and improves the process of transporting video to the studio, through the TVU Contribution Automation solution automating the planning, resource management, acquisition, transmission and routing as well featuring integrated metadata and contribution management.  

TVU CAS simplifies and helps efficiently manage the flow of media content from story creation to production. Featuring integration with leading MAM systems, TVU CAS allows broadcasters to easily search through metadata indexed content to allow for the creation of stories faster. CAS bypasses the need for inefficient and time-consuming search term logging done manually. 

At the heart of MediaMind is an AI real-time search engine, which from the start of the video production process, indexes all video. Using an entirely cloud-based model, complete with voice and object recognition, live and pre-produced video clips are located and indexed down to the exact frame and are instantly sharable. MediaMind automates the ability for different production groups to find the right content.  

“Since our first announcement of TVU MediaMind during April’s NAB show in Las Vegas, we’ve seen tremendous interest in MediaMind’s game-changing ability to automate and use AI to effectively create a story-centric workflow,” said Paul Shen, CEO, TVU Networks. “It turns the emphasis from the labor-intensive mechanics of production where one video is distributed to many into a customized story creation process where individualized versions of video are distributed automatically. 

“We’re thrilled to get another opportunity to showcase our MediaMind platform and other new solutions during NAB New York, which we always look forward to exhibiting,” Shen continued. “It gives us a chance to connect with our existing and prospective customers in the Northeast who were not able to attend NAB in Las Vegas or the IBC in Amsterdam. We can’t wait to demonstrate the synergy and power of our combined technologies for the NAB New York audience.”   

Aside from TVU MediaMind and TVU Producer, other innovations on display at NAB New York will be: 

- TVU RPS (Remote Production Solution) transmitter 

- TVU One mobile IP transmitter

- TVU Nano with Router ultra-compact, lightweight IP transmitter for high-speed network access

- TVU Grid IP-based video switching, routing and distribution solution

NEW YORK—Compliance monitoring has evolved over the past decade from simple broadcast logging to what is now a much broader range of applications that encompass monitoring, analysis and even revenue-generating functions for broadcasters, OTT providers and MVPD operators. Today’s digital tools have largely replaced the VHS long-play tape recorders to log transmissions, and other new technologies promise to only increase how broadcasters and others can utilize and even monetize what was once just compliance monitoring.

“The need for compliance loggers comes from the legal requirement to log all broadcasted content,” said Ken Frommert, president of ENCO, a Southfield, Mich.-based provider of broadcast technology systems. “This need, arising from the national telecom regulators, such as the FCC, did not change. However, the ever-increasing economic pressure on the same broadcasters, coupled with the fact the compliance loggers are an economic liability with no real business benefit, will necessarily force a technological evolution that will change the loggers into becoming economically beneficial.”

FROM BROADCAST TO OTT

Compliance monitoring is now at a massive transformational stage.

“To start, while compliance services have their roots in traditional broadcasting, we’re seeing the need for compliance services to not only cover broadcast, but also OTT content,” said Ariff Sidi, chief product officer for content delivery network provider Verizon Digital Media Services. “An increasing number of services now offer the ability to monitor OTT streams, and that trend will continue this year and beyond. We’ll see monitoring capabilities enhanced to cover the full path—from ingestion to delivery and through to the end user experience as well. Second, there will continue to be a major shift from compliance monitoring services being hardware-based to cloud-based.”

Even traditional compliance monitoring is now changing in terms of the content being monitored, the level of detail and even how long to store the results. Much of this wouldn’t have been possible just a few years ago.

“A simple studio-based ‘Air Recorder’ at the output of master control, keeping recordings for 30-90 days is insufficient,” said Ted Korte, COO of Qligent, a Melbourne, Fla.-based developer of broadcast monitoring and QoE technology. “More and more functions are either being outsourced or moved closer to the end user, and so should compliance monitoring. Having a number of monitoring points covering each of the delivery platforms, is a much more accurate representation of an audience and thus creates a more comprehensive compliance verification. Since new regulations are likely to evolve it is also a good practice to record all the associated metadata with the media and to record everything for extended periods of time to allow post verification analysis and audits.”

ORIGINS IN CALM

One of the key components of compliance monitoring is that audio levels are consistent as required by the Commercial Advertisement Loudness Mitigation (CALM) Act, which barred TV commercials—as well as other content—from increasing in volume at commercial breaks. Since the introduction of CALM it has been mostly “all quiet” on the audio front as broadcasters have mostly complied, but new technologies would bring up similar issues.

“Commercial loudness has been an issue since the 1980s, but finally modern audio systems really made it noticeable,” said William T. Hayes, director of engineering for Iowa Public Television and president of the IEEE Broadcast Technology Society. “Today the issue is how people consume content in other ways, and these services don’t follow the rules set in place by CALM. The major streaming services such as Netflix and Amazon Video are pretty good, but then you can instantly tell that YouTube videos and others aren’t CALM-compliant.”

For audio compliance, self-regulation could be one solution, but new technology at the consumer level might also solve the issue.

“The reality is that everyone who has a service that provides video and audio knows they need to do something,” Hayes added. “But self-regulating may not solve all the issues. However, smart TVs could be made to automatically adjust volume to a consistent level.”

FULL STREAM AHEAD

In a recently released white paper, Mediaproxy, a provider of compliance, monitoring and analysis technology noted that as the industry has to deal with an increasing number of transmission platforms that span from traditional broadcasts to OTT logging and monitoring paradigms have also shifted.

“It’s become apparent that with an increase in scale, the tools have to extend in versatility. Making sure that the right content is transmitted at the right place and time across the different outputs is paramount. Loggers are hence expected to have advanced video analysis features like content and source matching” said Erik Otto, Mediaproxy president. "However, this also means that OTT providers will have to address challenges, as streaming doesn’t fit in very well with traditional compliance monitoring."

“Linear and nonlinear streaming is very different as to how it is delivered and consumed, making end-to-end visibility and control much more complex,” said Korte at Qligent. “Streaming is changing all the usual points of reference such as primetime, demographics, DMAs, and even the definition of ‘live’.’’ All this new technology and opportunity will also invite new regulations regarding privacy and data protection [end users], fraud [i.e. advertising], piracy [i.e. rights], ratings [social media], etc. from multiple government agencies as well as vendor/service provider SLAs.”

Yet, unlike in a traditional broadcast, which typically has one version of content delivered to all viewers and with differences often the result of local market requirements, OTT could enable content providers to deliver innumerable discrete streams delivered to users—differentiated by region, device, preference or other factors.

“This same ‘one-to-one’ approach can be applied to compliance monitoring systems, too,” added Sidi. “In addition to monitoring the entire cloud workflow, it can check streams by region or other critical factors.”

The downside for content providers is that these opportunities could require more work.

“Budgets haven’t gone up,” and companies are not hiring replacements when employees leave, according to Hiren Hindocha, CEO of Digital Nirvana. “However today’s new compliance monitoring tools can address those requirements.”

AI AND MACHINE LEARNING

Technology could certainly play a major role in this evolution of monitoring and logging, and could take advantage of artificial intelligence or machine learning, which could more easily manage the influx of “Big Data” that is created at a near constant rate.

“The big trend is mixing technical compliance with content compliance,” said ENCO’s Frommert. “The way we see things, the loggers are producing enormous amounts of video data. This ‘Big Data’ is just sitting there for months until it is deleted and replaced by newer data, with no effective use. The use of Big Data technologies is gradually enriching the value of these video records and adding a number of useful functions.”

This, in turn could change compliance monitoring from something required and which can be costly, into a potential revenue stream instead.

Compliance tools are required by law, and thus become an expense for broadcasters, Hayes said, who added that today, AI can turn compliance into a value add instead.

“AI can analyze your audio for CALM compliance, but could also make it easier to ensure that closed captioning makes sense, including that the right version of a word is used. But it can also turn it into a profit center as it becomes a service, whether that is a tool for viewers to find content, or for those in commercial broadcasting so that advertisers and vendors can track their results.”

This could include automated detection of commercial ad spots, which could replace what has been a manually intensive work.

“This technology [AI] can scan many channels and provide very accurate affidavit reports on the number of ad spots,” added Frommert. “Similarly, the ads watching technology can also analyze competing channels and provide comparisons channel to channel on the number of ads sold and aired.”

AI could further automatically create captions and subtitles via speech recognition technology, while also utilizing rich metadata for searching and organizing the vast archives of video, which are stored on-premise or in the cloud.

“This can make it easier for people to find the content they want, as it is needs to be available to whomsoever, whenever they want it,” said Hindocha. “Metadata is really ripe for AI and machine learning, as it can help the viewer decide whether they want to consume that content. Metadata, as a side of the logging, helps you slice and dice the variety of content that is out there.”

BURLINGTON, MASS.—In technology circles worldwide, artificial intelligence is a dominant topic of reflection, research and increasingly, implementation. AI is delivering real value in several areas of the media production and distribution chain, but applications are limited presently. Other areas are showing tremendous potential and are now being explored. While it’s only a matter of time before the technology and our collective expertise overcome current limitations, it’s interesting to note where we are now and where we’re heading.

ENHANCING CONTENT DISCOVERY

The most obvious application of AI today is around automated metadata extraction or content “indexing.” The process of taking content, applying an algorithm to derive time-based metadata that is then registered in an asset management system is greatly enhancing content discoverability. For example, an algorithm that converts speech to text aligns each word in the text to markers within the content, making it possible to locate where a certain word or phrase is spoken.

It’s also possible to index content to find voice signatures that identify where a certain speaker is speaking. Now you have two layers of time-based metadata that allow fine-grained searching. You can keep adding layers or “strata” that enable even more detailed searching. For example, you may want to search for any time a particular public figure said certain words within a specific time frame. AI algorithms are so advanced they can do more than identify faces—they can infer the mood of each person at any given time. As you apply more AI algorithms to a content library, you add more strata of time-based metadata, enabling increasingly sophisticated searching, while automating a time-intensive task that’s difficult and prohibitively expensive to achieve with humans.

Perhaps even more valuable is the ability to discover valuable content long after it has been archived. Often, we don’t know in advance what content will be significant. AI can help uncover valuable assets that are hiding in plain sight within a content library. With enough metadata strata you can triangulate in to find content you may not even know you have.

Often confused with speech-to-text but operating differently, phonetic search has extraordinary promise. A time-based indexing algorithm can convert text into phonemes that take you right to specific locations within a piece of content. The end result is that you can conduct keyword searches on libraries never before transcribed. The ability to find clips/content phonetically works extremely well and is in products now on the market.

AUTOMATING QUALITY CONTROL

Another significant AI use case is automated quality control. AI-driven QC solutions can churn through a library of clips at one keystroke and analyze a broad range of quality parameters. It can show, for example, whether a program master that is targeted for French audiences actually has French language audio tracks, eliminating the need for someone to watch an entire show track by track to know that it’s the right version all the way through—a tremendous time and resource savings.

AI can also be used to ensure the accuracy of closed captions. Without AI, manual caption checks are required. Sometimes captions are wrong in the program master or aren’t in sync with the audio. AI quality control can confirm not only that captions are there but also that they’re correct. Some QC solutions can even make needed timing corrections.

An application at the nation’s leading over-the-top media services provider shows precisely how this type of QC is implemented. One of their brand differentiators is quality, so the company runs all content through a gauntlet of automated QC services, such as audio levels and visual clarity, identifying whether a host of criteria are met to ensure the highest quality standards.

A NEW MEDIA SUPPLY CHAIN

In addition to enabling better search and quality control capabilities, AI is also being used for operational business intelligence. In the pre-digital days, the media supply chain was extremely disconnected, with all steps of the process separated by physical media such as tapes. Today’s workflows are much more integrated and hold tremendous potential for operational efficiency.

But an understanding of the process is critical. To optimize your efficiency, you must track what people are doing and how they’re doing it in ways that inform the operational view of the media supply chain. With this kind of business intelligence, you can see where your pipeline is bogging down. Where are the choke points? Is it a lack of ingest capacity? Or if 30 percent of time in edit suites is taken up by rendering, it may be time to offload editing to a render farm.

Through an integrated AI-enhanced media production platform, it’s possible to measure all operations. How many people are using the system? How many are working on a specific project? Where are the technical bottle-necks and so on? For the most part, this kind of data has been impossible to collect and analyze—until now, media workflows have largely been a black box. But as media organizations seek to do more with less, more transparency into day-to-day operations is critical to garner operational metrics. AI can provide deep business intelligence to optimize production workflows.

AUDIENCE ANALYTICS

It’s clearly an advantage to have intelligence and analytics about your audience and their viewing habits to inform business decisions. Beyond the production stages in the supply chain, AI has perhaps even more potential for optimizing content distribution, providing insights about consumption patterns that are impossible to glean otherwise.

You can see the potential for news outlets and broadcasts. As news has become a 24/7 web and social phenomenon, news organizations and journalists need to know what’s trending and what news is breaking. Understanding events in the broader world context can influence how stories are assigned and prioritized. Today, specialized cloud-based service providers use algorithms to comb the web and synthesize trends, tracking information that may be relevant to a news topic from different information sources as they unfold. This data can be dynamically updated and available to journalists in a dashboard view, for example. AI that informs the creative process by analyzing downstream consumption patterns presents extremely powerful application possibilities.

MOVING FORWARD

The major challenge for more widespread AI adoption is not developing the algorithms, it’s how best to use and integrate them. As we add more and more AI capabilities, how do we harness the power they hold in a way that gives tangible business benefits? How do we make the user experience elegant in light of growing dimensions of data? These are questions that will be addressed as the technology progresses and implementations evolve.

In short, AI is an area that’s ripe for innovation. Business models are still immature as companies assess how best to productize AI so it makes business sense both for users and providers. In the end, the closer we to get to the craft of storytelling the more of a human endeavor it becomes. AI is a tool that assists with storytelling; it doesn’t replace it.

Tim Claman is chief technology officer/vice president of product management for Avid.

In today’s fast-paced media environments, more new content is being created than production teams can possibly manage without specialized tools. At the same time, the clock is ticking for digitizing historical content that exists in legacy, analog formats like tape before the original content degrades. It’s critical that all of these assets be logged and tagged so that they can be found easily, but teams have no time to do this essential work.

In addition, the current generation of media asset management tools has evolved in an environment where they have been starved of metadata. As a result, content teams’ options are limited to pulling technical metadata from media files or streams, extracting meaning from file and folder names, or manual logging.

UNLOCKING THE POTENTIAL

Artificial intelligence is beginning to change how media organizations meet these challenges. A new and emerging breed of AI platforms for media analysis, when paired with leading-edge media asset management tools, offers great potential for transforming media workflows and making it easier than ever for operations to access, manage, and archive tremendous volumes of content. Through powerful tools such as speech-to-text and automatic language translation, AI engines bring new power to the MAM task of logging and tagging content—with the ability to tag assets automatically based on attributes such as people, places, things, and even sentiment.

But hold on: a few caveats

It sounds almost too good to be true: suddenly you can unlock the potential of all of your content and make it immediately searchable, reusable, and monetizable. At last, you can get some traction on those digitization projects and get a better handle on all of the content in your existing library! But wait—while the potential exists to realize these benefits someday, the truth is that the technology needs to overcome some issues in order to become mainstream.

One area that needs improvement is accuracy. While AI analysis is getting better all the time, particularly with speech-to-text offerings from players such as Google, Microsoft, Amazon, and IBM, fine-tuning is still needed. For instance, the engine might not be able to distinguish between U.K. or American English, and abbreviations and jargon are likely to generate mistakes. The industry is still working on easy methods to train the AI engine to recognize these language variations and correct mistakes. Also, for image or video analysis, the sophistication of AI tools varies considerably. Some platforms offer only very basic video analysis, meaning the best way to capture metadata for people, places, objects, and sentiments is to make a set of image sequences and analyse them manually.

AI aggregators can help users avoid some of the costs and complexities of setup by making it easier to choose the right AI engine for a specific task. But even so, picking the AI tool that’s best for a given activity is not trivial. At the same time, cost structures across the industry are far from transparent, making it difficult to work out the total expense of applying AI to a media library. It’s a multi-step process: first, you have to figure out how to get your content into the AI engine—which is often in the cloud. That might involve having to create a video proxy, separate the audio files, create an image sequence, and other steps, and then uploading the content and managing its lifecycle. Should you leave the content on the vendor platform or delete it to save on storage? Is it in the right format for the AI engines to understand? Which AI tool should you run, and is there a separate cost for each style of analysis? There might be different price tiers for different content formats; for instance, 4K assets might cost more. With each vendor having its own price list, it’s pretty difficult to compare apples to apples.

Also, the technology is advancing so quickly that any AI analysis done today may have to be refreshed later, as the tools improve. Managing these refreshed data sets, especially if they have been corrected or updated by a human after the original analysis, adds another layer of complexity. And of course security is a concern, especially if the data is uploaded to cloud providers.

THE AI-MAM CONNECTION

As these powerful AI technologies continue to mature, strong media asset management capabilities will become increasingly important. On the metadata side, tools that can store, search, and easily correct a huge volume of time-based metadata are crucial. Good metadata and user interface design are vital to keep the system from overloading users with too much information. And on the workflow and automation side, feeding the AI engines with the right data and automating the analysis, while keeping down costs, will separate the true enterprise offerings from the also-rans.

So what might an AI-powered MAM solution look like? One approach is to supercharge the MAM system’s logging, tagging, and search functions through integrations with leading AI vendors and aggregators, such as Google, Microsoft, Amazon, and IBM. Integrations with best-of-breed AI platforms and cognitive engines could allow the MAM to leverage advanced AI-based speech recognition and video/image analysis, with the flexibility to be deployed either in the cloud or in hybrid on-premises/cloud environments.

Here are a few of the advanced capabilities that could result:

• Speech-to-text, to automatically create transcripts and time-based metadata

• Language translation

• Place analysis, including identification of buildings and locations without using GPS-tagged shots

• Object and scene detection (e.g. daytime shots or shots of specific animals)

• Sentiment analysis, for finding and retrieving all content that expresses a certain emotion or sentiment (e.g. “find me celebrations (in a sports event)”)

• Logo detection, to identify when certain brands appear in shots

• Text recognition, to enable text to be extracted from characters in video

• People recognition, for identifying people, including executives and celebrities

THE NEXT FRONTIER

Of course, these capabilities are just the start. The MAM system can also be a powerful tool to train and improve AI engines; e.g. content manually tagged in the MAM could perhaps be used to identify the executives in a corporation. The MAM could use this manual tagging to train AI engines to do a better job of logging and tagging new content.

The industry is being transformed by AI and the explosion in sometimes low-quality metadata. Only the most powerful, flexible, easy-to-integrate, secure, and scalable MAM platforms are embracing this challenge and will thrive.

In the right hands, AI becomes the key that unlocks the next generation of MAM technologies.

Dave Clack is CEO of Square Box Systems, makers of the CatDV media asset management solution.

Most content owners would agree that manual tagging and cataloging of assets is a time consuming and expensive activity that can stymy the usefulness and profitability of media assets. Today however, audio-visual content can be transformed into dynamic assets using metadata enriched with a combination of Artificial Intelligence (AI) techniques and manual processes.

Beyond basic tags that allow for different variations of the same word (e.g. singular and plural) or multiple tags for the same concept, automated AI technology offers more powerful and faster capabilities that can aid content discovery for easy re-use and greater productivity. For example, content can be auto-tagged using Object Recognition as well as Face/Location Recognition technology to further enrich metadata. AI metadata can also be used to discover specific areas where compliance issues have been spotted. Improvements in Speech-to-text and Image Recognition algorithms are further automating parts of the metadata process, making it increasingly useful for both new and legacy content use cases.

AI-ENABLED MEDIA ASSET MANAGEMENT

Here is an example of how AI technologies are already starting to become an integral part of commercially available products and services. AI functionality has recently been integrated into CLEAR, a cloud-based Media Asset Management (MAM) solution by Prime Focus Technologies. It helps Media & Entertainment (M&E) enterprises leverage both AI-generated and manually logged metadata to maximize content discovery. This makes it faster and easier for users to sort, locate, and re-use their assets for creating compelling stories from a vast content repository.

Importantly, CLEAR empowers users with the freedom to access the entire galaxy of AI/machine learning capabilities (AWS, Azure, Google, IBM Watson to name a few), as appropriate for their downstream use case.

Useful functions for AI-driven visual analysis include character identification as well as recognition of objects and actions. Ideally, AI video analysis should also recognize text within the image, such as street signs, advertisements, and products. The video AI functionality in CLEAR, for example, includes the capability to recognize human faces, their genders and approximate ages. It also recognizes brands and logos, along with well-known landmarks, and presently, to some degree, other objects at a level that includes phones, tables, vehicles, and animals, for example. Additionally, the system can also recognize basic interactions between these objects, such as a human running, dancing, or smoking as examples, and can detect the general facial emotions of people and events, such as being able to tell the difference between a festive party and an intense group discussion.

While all AI-based functionalities at present require human review and quality control, one of the key characteristics of AI is its ability to learn and improve over time, so we expect these functions to continue to evolve going forward.

METADATA IMPLICATIONS

If a collection of media assets could be compared to a file cabinet full of important papers, then metadata would be the labels on the folders. While it is true that the papers themselves are important and valuable, it is the labels that make it possible to locate the particular paper you presently need quickly and effectively.

In a similar way, as content is analyzed and then augmented with metadata, value is added because the ability to locate the desired content and understand important features of that content is enhanced. The value of metadata in our current time is even further enhanced because the quantity of content that already exists, along with what is now being created, is so extensive. If AI techniques can be used to help human evaluators review new or existing content and create valuable metadata, then it can help organizations unlock additional value in these assets.

There are several ways that using AI to improve metadata processes can help an organizational business case. Making the most of digital content often requires M&E enterprises to move beyond basic asset management and embrace new-age, Cloud-based MAM solutions. Such solutions, with extensive data models and built-in tools for cataloging and metadata tagging, help organize content in useful ways, make it easily accessible, and open up new avenues to monetize assets. Ultimately, they streamline and improve the overall efficiency of digital content creation, boost monetization, and drive creative enablement.

Here is a recent example of how more detailed and accurate metadata gave some content providers an advantage when broadcast content regulations changed. Whenever such regulations change, there is a time consuming and stressful burden placed on the M&E industry to quickly come into compliance. In this case, it was legislated in India that all smoking scenes must be prefaced with a warning message about the hazards of smoking. When this regulation came into effect, detailed and accurate metadata let some content providers quickly identify every scene that included smoking. Armed with this information, broadcasters could then choose to either remove the scene, or add the required warning message.

Looking at content from another angle, content creators can also depend on the use of metadata to locate existing materials for new projects. Documentary film makers, news organizations, and investigative reporters are just a few of the kinds of content creators in this category that frequently look for content relating to specific themes, public figures, locations, and similar categories. When a famous or influential person passes, or a current event warrants a special report, detailed and accurate metadata describing existing footage and media about the topic can make the search for this material far faster and more effective.

CONCLUSION

The integration of MAM solutions with metadata management tools, extensive data models and AI-based functionalities, hand-in-hand with human quality control and judgment, will help organizations achieve the effective labeling of vast quantities of existing and new content. And, the improved accuracy and efficiency will deliver, in turn, improved business efficiencies and results across cataloging, editing and mastering operations. By making it possible to find content faster, easier, and more accurately, AI-enhanced systems will create new revenue opportunities.

T Shobhana is Vice President and Head of Global Marketing & Communications for Prime Focus Technologies.

TV measurement is going through big changes, according to a new whitepaper from TiVo.

After years of audience estimates being based on small samples or panels of viewer, big data has come to TV in the form of set-top-box data and automated content recognition.

Those new forms of data “are set to radically change the way marketers, networks and broadcasters measure TV viewership, plan media buys and execute ad campaigns,” TiVo says.

While not mentioning Nielsen by name, the report says that using old-school TV measurement—in a digital age is creating skepticism about measurement from networks and marketers.

“The root of this growing skepticism is the continued reliance on small panels – numbering in the tens of thousands of households – to represent the TV viewing behavior of 120+ million U.S. households. As one interview subject with a background in statistics put it, ‘modeling the entire population from such a small sample necessitates lots of assumptions and risks many inaccuracies,’” the report says.

Nielsen stands by its approach.

“To accurately measure anything, you need to be able to directly observe it and sample it in a representative way. That is why our industry standard panel is and will continue to be the foundation of television measurement,” said Kelly Abcarian, senior vice president, product leadership at Nielsen.

“Panels are purposely built for persons level media measurement, while big data is a byproduct of a machine. So not only is this device level data instead of persons level data, but it is also merely an unrepresentative bigger sample. A sample that is incomplete, biased and discriminates against those excluded from its measurement is a problem regardless of its size. If big data comes more from high rises, city dwellers or apartments instead of private residences, meaning you don’t get data from all parts of the market and you don’t have a truth set that can be used for adjustments, you can’t pretend it doesn't matter,” Abcarian said. “As we have seen in digital, when data is not accurate or transparent, this results in under delivery of the ROI goals and advertisers will either lose confidence in television as a medium, or the agency, or both.”

Measurement is a more complicated proposition now than when there were three networks and families watched on TV set in the living room. Now there are new platforms and devices and an explosion of content. Marketers also have experience with digital media, which promises precise measurement, targeted audiences and feedback on how users responded to content and advertising.

TiVo says the new forms of viewing data are being collected at scale, are highly granular, are being built for new viewing methods and can be integrated into marketing models.

Set-top-box data is the largest source of TV viewing data at this point. One downside, however is that because of the fragmented nature of the cable industry combining data from different operators can be tricky.

“The biggest problem – and one that limits the number of companies willing to even tackle STB data aggregation – is dealing with the variety of file formats, data structures and levels of granularity,” the TiVo report said. “Concerns about how well STB data reflects the overall population persist, since 17%of TV households rely purely on over-the-air signals and an increasing number of younger households are cutting the cord.”

Data based on automated content recognition is available from fewer viewers at this point.

“Manufacturers are only beginning to share ACR data with other companies or withholding it completely, limiting the reach of ACR data available today,” the report says. “Vizio is a notable exception, but census-level ACR data availability would require active participation from major manufacturers – and none appear willing to cooperate with others.”

On the plus side, the TiVo report says ACR data collection benefits from its flexibility, allowing the capture of viewing data across linear, DVR and smart TV app viewing, although there may be data gaps in VOD and some long-tail content due to watermarking requirements.

It is tough to combine set-top-box viewing data with commercial logs, the report says.

“ACR technology enables the tracking of ad units at the individual device level without having to rely upon commercial as-run logs. This allows for faster reporting on ad delivery and more accurate data for addressable TV advertising. Nonetheless, taking advantage of this technology requires advertisers to incorporate ACR-ready watermarks during the production and distribution process,” the report said.

The presence of clear, trackable consumer conversion activity is essential, as with all attribution, the report added.

While the new methods of audience data collection have advantages, adopting them faces hurdles, the report says. Those included:

• Industry inertia and the currency conundrum: “Despite the limitations of traditional, panel-based TV measurement, it is the currency by which billions of dollars of TV advertising is bought and sold. And, it is no small task to shift from today’s gross rating point (GRP)-based TV buying model to the impression-based buying model required to maximize the benefits of next-gen TV data,” the report says.

• Data quality transparency:For next-gen TV data to become the new standard, data vendors will need to offer greater transparency into their collection approach, modeling methods and data sources.

• Consumer privacy and regulation: Digital data collection and targeting has long been subject to a set of self-regulatory guidelines, but the nascent nature of TV data collection means these standards are still evolving.

Looking ahead, greater data scale, quality standards, privacy protections and ease of access through aggregation will enable marketers and networks to more seamlessly leverage the strengths of both set-top-box and ACR data,” the report said.

“People-based marketing will become a reality as marketers and networks stop evaluating TV ad exposure in aggregate demographic groups and instead map individual TV viewing behavior to cross-device marketing exposure and attributed outcomes. Now that marketers have these capabilities across other screens, they will also demand them from TV,” the report said.

One of the more intriguing storylines of this year’s NAB Show was the realization that the biggest impact that automated intelligence will have on the television industry is that AI will allow production to change its focus from a program-centric process to a story-centric process where content is automatically produced, targeted and distributed to the viewer. In short, using AI will allow broadcasters to identify and tailor video content to individual tastes, and moreso, allow content producers to better monetize their assets.

This concept—known as “Media 4.0” where AI is used to automate the production and distribution of media to any device on-demand—has been around for several years. Whereas Media 1.0-3.0 represented the evolution of media from film and theater to radio and television broadcasting and finally to the current IP-based landscape, 4.0 can best be illustrated by the music industry’s use of big data and the cloud to provide the personalization of media consumption down to a granular level, according to Paul Shen, founder of TVU Networks, a provider of IP-based media production and distribution tools. Using Spotify as an example, Shen noted that the music streaming services’ popularity is not necessarily because of the depth of its library, but by the ability of its sophisticated algorithms to learn consumer’s musical tastes, creating “personalized radio.”

“I have 90 days of music collections on iTunes and I don’t listen to it at all, I use Spotify,” Shen said. “The reason is easy. I don’t have to do the work. Spotify offers the content the way I want it. It uses artificial intelligence to create the playlist.”

TEN YEARS BEHIND

This use of AI has helped the music industry and IP-based video services like YouTube and Netflix to leapfrog 10 years ahead of broadcast, according to Shen, who believes that current production and distribution methods are bogged down by the human-driven assembly-like process that can be changed to an automated process by using AI and machine learning with the unlocked power of metadata. To overcome these hurdles, TVU introduced its MediaMind cloud platform at the 2018 NAB Show.

MediaMind puts the concept of Media 4.0 into practice by using AI, and object and speech recognition to maximize the use of media assets, particularly in live production. This “smart media factory” works within the existing video production workflow but enhances it by automating the ability to identify video content and push it to a variety of platforms.

Shen uses President Trump’s recent North Korean prisoner release ceremony as an example of how the MediaMind concept could be put into practice.

“There were a lot of cameras there that were recording for hours, waiting for the moment [prisoners debarked from the plane], but they didn’t know exactly when,” Shen said. Using AI-powered facial recognition technology enabled by MediaMind, the cameras and mics could be automated to just focus on particular shots of interest to individual viewers, for example.

This CAS (Contribution Automation Solution) is one part of TVU’s MediaMind concept, but another equally important concept is the automation of the entire planning, acquisition and routing process. It integrates with the news system, router, camera, transmission devices and alert system. Once a story is created, the system will help manage resources by sending an alert message to the corresponding reporters and crews as well as all of the connected devices, such as cameras, transmission devices, routers and recording devices. Once content arrives into MediaMind, it becomes instantly searchable via metadata that is tagged to the content from acquisition onward. This advanced search capability gives MediaMind the ability to push content to the producer, rather than the producer having to manually access it.

“A producer says ‘I want to write a story about the return of the three Americans from North Korea,’” Shen notes as an example,” the content will appear next to him rather than him having to go search for it.”

IN PRACTICE

The MediaMind API is already being integrated into the workflows of 4-5 of TVU’s approximately 2,500 TV station customers. Shen says customers can pick and choose which parts of MediaMind they want to integrate into their processes, which covers the production chain from acquisition, editing, processing and distribution.

In today’s live televised production environment, nearly 99 percent of the raw material goes unused and therefore, un-monetized, according to Shen. Current production processes are tedious and don’t meet the demands of today’s viewer, who is used to using social media to provide the type of feedback that allows content to automatically be “pushed” to them.

Shen thinks Media 4.0, illustrated by TVU’s MediaMind platform, represents the future of live production—an integration between production and consumption—where viewers’ tastes, rather than the producers’ whims, will drive a more “story-centric” workflow.

“Media 4.0 is going to fundamentally change what’s needed to make the TV industry meet the demand of the audiences,” Shen said. “That’s what this whole initiative is about.”

There is nothing new in the distribution of linear TV over the internet; TV Everywhere (TVE) is pretty much demanded by consumers and is now considered the norm. Traditional broadcasters are forced to keep up with evolving viewer habits by making content available online pretty much immediately and on any device.

But in almost all cases, they are doing so without inserting the necessary signalling which indicates where within a linear stream advertising and other content segments begin and end, and which segments can, must, or must not be replaced with alternate content. Broadcasters already consider providing content Over-The-Top (OTT) a cost of doing business–they know it is part of their future. However, without the right signalling, it is virtually impossible to realize the maximum monetization OTT has to offer broadcasters. Moreover, without the right signalling it is extremely difficult to provide the seamless professional viewing experience broadcasters and viewers expect.

By failing to invest in the technology which can make OTT so lucrative, including signal delivery and correct metadata signalling, broadcasters are not making the most of the opportunities presented by OTT, of which there are many.

THE OTT CHALLENGE

Although OTT should be looked at as an opportunity (as long as the right technology is in place), distributing linear content over the internet is considerably more complex than OTT VOD or TVE distribution. Even the process of reformatting content for OTT is time-consuming, let alone the challenge of correctly adhering to content distribution rights across various devices and regions.

OTT distributors must now programmatically and automatically react at the last second if a piece of content cannot be distributed to viewer X on a cell phone in California because it does not have the right to do so. Without the correct signalling in place to trigger the replacement of this content with something else, the broadcaster could be fined and the OTT distributor may just default to putting up a “slate” to blackout the content. With the current level of competition for viewing time, modern viewers simply won’t wait for this to be resolved and are likely to switch off or watch another video stream.

Linear TV advertising is big business with transactions for the priciest slots coming in at over half a million dollars. Broadcasters are able to cover the astronomical cost of putting a channel on air with the revenue generated from this advertising. But making this content available online could involve costly duplication of workflows and costs, which are not covered by today’s OTT advertising and subscription revenues. The sad fact is, however, that it doesn’t have to be like this for broadcasters, with OTT presenting a number of opportunities to add further value to their existing linear content.

[Read: Syncbak To Conduct 'First Ever' Live OTT Broadcast Using Local Dynamic Ad Insertion]

One of these opportunities is dynamic insertion, or more accurately, replacement of targeted ads. But again, this simply isn’t as easily done in the case of linear TV streamed OTT. For example, it makes no sense for ads broadcast on linear TV to remain with the content for OTT VOD streaming several days later. These ads may be stale and irrelevant by then. More pressingly, the replacement ads must be the exact duration of those needing to be replaced otherwise there will be a discontinuity (a black frame if the ad is too short, or cutting off the beginning of the following content segment if too long, for example). To do this seamlessly and to make OTT as profitable as possible, the broadcaster must be able to deliver the metadata, which signals the frame boundaries and duration of every content segment, to OTT distributors so ads can be replaced dynamically.

DELIVERING THE RIGHT SIGNALS

Broadcasters also have the opportunity to capitalize on the expansion of Nielson Ratings to include the first few days after a linear broadcast, in order to account for on-demand viewing. Linear content streamed OTT effectively has “two bites at the apple,” but only if broadcasters are able to making this content available almost immediately within the C3 window, and cost-effectively too.

Integration with broadcast systems, like media asset management systems, automation playout systems, on-air switchers, etc., provides the most effective way to access timing data associated with content segments. This way, information including timing, asset type and unique IDs can be signalled in-band or delivered out-of-band and applied to the video stream later, with no impact on the main linear broadcast workflow.

If a broadcaster were to extract metadata describing a linear TV channel, including the start and end of each content segment, and deliver these as SCTE 35 messages, it is possible to automate a whole host of value adding applications. This includes Dynamic Ad Insertion/Replacement, the creation of on-demand assets and even interactive TV. As this is done in an automated fashion, it removes much of the cost and labor associated with making linear TV available OTT and solves many associated business challenges.

For example this solves the issue of complex rights management. A policy can be triggered downstream when metadata within a content segment indicates that a particular piece of content must be replaced according to certain parameters as further defined using SCTE 224. For example, if a stream is requested by a viewer in country X or on a particular network, the metadata signalling can indicate that a different stream must be delivered instead.

With the ability to dynamically replace ads on-the-fly, thanks to correctly delivered metadata signals, providing linear content OTT can be a much more valuable process. This is particularly the case if these ads are personalized. In fact, a recent study found that ads within OTT platforms are 67 percent more effective per impression than those on broadcast or cable television. This is because it is much easier to personalize ads according to individual users when delivering content over the internet, as an individual stream is sent to each viewer.

Using additional metadata about users, such as location, viewing history, browsing history etc., it is possible using a distributed playout architecture to deliver much more targeted ads to specific users. Naturally this means the ads themselves are much more valuable and therefore profitable. As long as the timing information and metadata about each segment and ad is delivered, either in-band or out-of-band, to the last video server in the distribution chain, operators can make sure the ads they insert are seamless and accurate.

CONCLUSION

Broadcasters have very little choice when it comes to providing OTT content if they want to remain relevant. This does present many challenges as we have discussed, but none of these are insurmountable with the right technology in place to deliver signalling and metadata information. With these measures in place, linear broadcasters can take advantage of the applications that can make OTT an attractive proposition. Everything from content replacement, rights management, automated asset creation and dynamic ad insertion/replacement are all possible, increasing the value of linear content and opening up new revenue streams. OTT is no longer a cost of doing business, it is a lucrative business. 

BOSTON—As one of the nation’s premier public broadcasters, Boston’s WGBH-TV has a strong legacy of programming that it has provided to PBS and other outlets for decades. From the classic children’s series “ZOOM” to “Masterpiece Theater,” “Nova” and “Antiques Roadshow,” WGBH has enriched the nation’s library of television programming since it first signed on the air more than 60 years ago.

That program library contains thousands of hours of film and video in a variety of formats that has become increasingly hard to manage over the years, according to Shane Miner, senior director of technical services, who joined WGBH four years ago. Problems such as increasing rates of ingest, manual processes, no digital rights and inadequate media asset management led the WGBH IT team to search for a better solution.

“When I started, I noticed what I thought was an inefficiency around our archives,” Miner said. “It was too manual and we were going to struggle to scale as both the amount and size of media increased. I felt like it wasn’t providing producers with what they needed to be able to meet their shorter deadlines and quicker turnarounds.”

Initially WGBH looked into a cloud-only solution but found that it didn’t meet their workflow requirements because of long download times and egress charges for video became too expensive.

MANAGING CONTENT

The key to managing and maximizing the full potential of WGBH’s vast archive could be summed up in one word: metadata, according to Miner. This led him to consider object storage as a hybrid solution that provides the best of both worlds between a NAS/SAN and all-cloud system.

“You can’t find any video unless you have a good metadata structure to be able to search, so the idea with object store is that you can marry the metadata to the video as a single discrete object that gives you a ‘self-describing’ video file,” Miner said

In addition to enhanced search and retrieval, other advantages to object storage is that it is limitlessly scalable, and can be deployed in an on-prem and cloud environment. In the end this reduces costs by reducing the amount of time staff has to deal with physical media, an important consideration for a public TV station with a limited budget.

WGBH stored a large amount of its programming on LTO and the time consumed in accessing physical tape was a big consideration in cost reductions, according to Miner. “It’s a much easier process when it comes to people time and energy,” he said. “We don’t have to plan large projects around the changeover [from physical media]. Definitely object storage locally with the second copy in the cloud is cheaper than what we looked at over many, many years than a traditional LTO solution.”

WGBH’s current setup includes 3 petabytes of on-prem storage occupying 15RU in a data center that reduced physical space by 90 percent when compared to its tape library. It uses AWS Glacier for cloud/DR and AWS S3 for extra capacity. On-prem object store is based on the Cloudian platform that provides for editing and archive curation. Final versions are stored in Sony Ci.

DO YOU NEED A MAM?

Using object store reduces the need for a media asset management system, Miner said. Whereas search in the traditional archive was limited to just what was in the archive, object store allows content to be searchable as soon as media enters the workflow.

“What we’re hoping to do with object store is actually break apart the MAM,” he said. “Because we can store the metadata directly on the objects and then use the file system to search, you actually don’t need a big heavyweight MAM to handle all the processes.”

When Miner and his team were looking at implementing this new storage system, the station was in the process of launching its Public Media Management cloud-based master control system. Since PMM targeted centralized master control, the model lended itself towards more cloud-only storage since PMM only handled finished shows, according to Miner. For archiving, however, the requirements were different.

[Read: WGBH And Sony Partner On Cloud Workflow]

“The problem we have with our actual archive is that for example, ‘Frontline’’s hour-long show generates hundreds of hours of footage so storing all of that content becomes costly and cumbersome in terms of pull down cost,” he said. “So as we looked at the full archive, we’ll use Sony’s Ci to store our finished program material [for PMM], but that’s such a small subset of the actual full archive that it’s much easier to manage. But the two systems [PMM and the archive] are pretty disparate; central master control has its own archive of broadcast material that specifically focuses on master control and nothing else and this archive is specifically focused on WGBH and nothing else.”

Using metadata as the driver of the transition not only helps reduce physical space and the time station staff need to retrieve content, it also gives producers more capabilities in handling content through the production workflow. When you have archives that stretch all the way back to early Julia Child film reels, this illustrates how the massive potential of metadata can be used to maximize the value of such legacy programming.

“We take our archive to be our mission element,” Miner said. “We believe that maintaining our archive is a public good and that’s something we should do because it’s essential to our mission.”

LONDON--As guests arrive at the Royal Wedding on May 19, Sky News will be using machine learning technology provided by Amazon Web Services and two partners to name guests and provide additional background.

Fans will be able to access the ‘Royal Wedding: Who’s Who Live’ via the Sky News app or via skynews.com. The technology enabling this enhanced user experience and deliver this service at scale, is being provided by Amazon Web Services (AWS) and two AWS technology partners, GrayMeta and UI Centric. 

As guests make their way into St. George’s Chapel, Windsor, AWS will capture live video and send it to cloud-based AWS Elemental Media Services for multiscreen viewing optimization. An on-demand video asset, including catch-up functionality, will also be generated. In parallel, Sky is combining the GrayMeta data analysis platform with the Amazon Rekognition video and image analysis service for real-time identification of guests and tagging with related information.

[Read: AWS Elemental Launches Media Services]

Finally, Sky News is using the Amazon CloudFront content delivery network to unify the content for rapid distribution to viewers. UI Centric has designed and developed the front-end application and video player to enhance the experience and user interface accessed by Sky News viewers.

Keith Wymbs, chief marketing officer for AWS Elemental, the video division from Amazon providing the encoding and cloud services for the event, said that AWS has a long relationship with Sky, adding that the capability will give Sky some insight into the capabilities of machine learning.

“It’s really a way to explore what we can do in a more nimble environment where we don’t have to provide a traditional workflow where you’re bolting servers into a rack and dealing with all the related hardware,” he said. “This gives Sky an understanding for exactly how much demand is going to be there so they can do a lot more experimentation and be first to market with things that are exciting for the end user.”

Video content from the application will also be made available on demand after the event. 

LAS VEGAS—Another NAB, means another few trips around North and Central Halls and both levels of South Hall to check out a sample of what new technologies are leading us to the future of sports broadcasting. A big trend this year was the continued emergence of products that combined the power of AI with metadata to enhance and expand the fan experience.

HIGHER RESOLUTION, HIGHER FRAME RATES

On the image capture front, Emergent Vision Technologies (EVT), based in Maple Ridge, B.C., released a 25GigE version of its Bolt camera, and what it claims to be the world’s first 25GigE camera. 25GigE is the successor to 10GigE, a rapidly growing interface for machine vision applications. It provides the same benefits of 10GigE, but with a 2.5x increase in data rate, which leads to a 2.5x increase in frame rate.

As with 10GigE, 25GigE is a cross-industry standard which has been employed for many years and is managed and/or produced by the IEEE 802.3 working group. The standard is used in various sports applications, among others.

The 25GigE Bolt will be in full production in about two months, according to John Ilett, executive vice president for EVT. He added that the new camera “will take sports television to the next level after we got this point with our HR 10GigE cameras. The Bolt will be an advantage for our customers who need to take a camera into a stadium and connect it with long optical fiber runs, [who also need] higher resolution and higher speeds.”

NATURAL MOVEMENT

Also in focus at NAB was the high-speed Polycam Player, a software solution that integrates MRMC Motion Control, the Chryon Hego TRACAB and Nikon cameras. Clubs, leagues and venues need an automated, high-mounted and wide-angle video analysis solution at game time, and this robotic video capture system offers a high level of automation, flexibility and low-light image quality.

Using the TRACAB player tracking solution, Polycam Player physically moves the camera and adjusts the zoom and focus to automatically keep the team or the player in the frame. Unlike other systems which pan and scan footage from very wide angle camera arrays, the Polycam Player mimics the natural movement of a camera operator from locations which would be impossible to physically place a human shooter.

“The Polycam Player gives analysts and broadcasters a definite advantage at game time. The system uses Nikon cameras, which offer superior image quality, optics and low-light capture ability,” said Mark Suban, senior manager of Nikon Professional Services. “These cameras are housed inside MRMC robotic pods which, unlike other systems, actually move, zoom and focus the camera for maximum precision. The combination of [the various] technologies working together helps ensure that there is no other automated system that can offer this level of flexibility and performance.”

TAP A FINGER

Sportscasters have been able to roll the videotape for more than 40 years, but Capticast SHARE, a Microsoft Azure-based broadcast solution, allows a presenter to take a sportscast in directions previously unattainable. Its charm is that, while managed by producers in the control room, it leverages the Surface Hub as a large format, on-air delivery platform.

The technology allows the sportscaster to deliver the news on-air through an intuitive, broadcast-centric, camera-ready front-end, complete with telestration, direct manipulation and navigation tools. Images, videos and maps are delivered in a single application experience to tell a cohesive story. SHARE comes standard with neo 360 AIR, which allows for full control of the speed, direction and frame-by-frame playback of video―with just a swipe or the tap of a finger. Capticast has partnered with Fox Sports to enable fans to play slow-motion video of live events on their smartphones. “For the first time ever, sportscasters (and newscasters) have full control of the video content shown on-air,” said Capticast CEO David Borish, “This comes at a time when broadcast news organizations are fighting to maintain market share in the face of online competition, and need more engaging storytelling that capitalizes on the live nature of the on-air experience.”

TOUCH & PINCH

Along a similar vein is Xeebra, from EVS, which allows not only the gang in the truck but game officials to easily review a play from numerous camera angles, then select the most relevant on the system’s touchscreen or via a dedicated controller. Users can also zoom into selected images with a simple touch-and-pinch gesture to review every angle in detail quickly, and in complete synchronization.

Xeebra’s client/server architecture guarantees flexibility and allows the user to deploy it wherever needed: on the sidelines, in a venue production control room or a remote officiating facility. The system also uses machine learning, enabled by EVS’s new VIA Mind, to calibrate the field of play; that allow users to accurately overlay an offside line for video assistant referee operations in soccer, for instance.

With a flexible client interface allowing multiple display screens, content is always synchronized, with browsing of up to 16 HD cameras. Xeebra also supports the ingest and browsing of high-frame-rate (SuperMotion) cameras.

“Xeebra brings replay technology to video refereeing for the likes of VAR workflows in soccer,” said Sebastien Verlaine, marketing and communications manager at EVS. “Users benefit from the system’s ability to integrate with broadcast-centric workflows already in place, and its built-in artificial intelligence helps operators overlay graphics, like an offside line, with the highest level of precision.

“The machine learning technology implemented into Xeebra 2.0 automatically calibrates the field of play―something that’s time-consuming and subject to error, if not done carefully,” Verlaine continued. “This doesn’t require any pre-game setup or the deployment of additional sensors or cameras; it uses only the images from the live broadcast camera feeds. As a result, operators can confidently add an offside line to show the referee when a player is offside, making sure the right call is made.”

A SMART APPROACH TO LIVE

Artificial intelligence, machine learning’s cousin, plays an important role in the debut of a new platform from Tedial, which launched its SMARTLIVE live sport solution at the show. SMARTLIVE marries AI to an innovative metadata engine to tighten integration between the back office and archives. This allows for more highlights, during or after an event, and allows a specific story to be delivered to a targeted audience via multiple platforms including social media, increasing the potential for significant growth in fan engagement.

SMARTLIVE is 100 percent compatible with PAM providers such as SAM or EVS which means all business processes can be orchestrated on top of an existing PAM, including the use of historical archives in live production. It can then manage the media life-cycle or media movements between different sites.

SMARTLIVE brings historical archives into PAM logs live in the format the PAM can immediately use, fundamentally reducing production preparation time, and allows broadcasters, rights holders, clubs and federations to significantly increase the number of highlights created during or after a sport event with less staff thanks to its automatic highlight engine and AI integration.

“The people in the truck, they’re trying to make 10-15 highlight clips per game,” said Jay Batista, general manager, U.S. for Tedial. “We’re going to try to make that 75-100 so they can use them over and over and create more fan engagement. It takes less than a second to quickly assemble 15 clips. That speed and that nearness to the live production is what separates us from the other vendors.”

THE FUTURE IS NOW

Another company that taking a similar approach to sports production is Pasadena, Calif.-based StainlessCode, which has developed AI that can watch and understand sports media, extracting metadata automatically, in real time.

The software, Stainless Autologger, automatically processes video by training neural networks to identify and extract metadata from the visual content, while simultaneously accessing external data sources describing the content (for example, sports play-by-plays, Twitter and Facebook feeds, robot sensor data logs or even law enforcement reports). The system can then combine the data with the video automatically, providing rich, time-indexed, semantic metadata.

The company has one patent and second pending. “This approach provides a depth and accuracy for video metadata that is not achievable manually,” said Dan Stieglitz, CEO of Stainless Code. “With the explosion of video content that is happening online, some measure of automation is necessary to fully extract the value of the video for publishers and consumers.”

TWO DOZEN ANGLES

With the acceptance of video assistance in making calls on close plays across the sporting world, the pressure to “get it right” is more intense than ever; even while game officials have access to multiple game feeds, however, that bonus still doesn’t always result in the correct call.

Enter the videoReferee, from Torrance, Calif.-based Slomo.tv. The technology can share up to 24 angles to meet even the Fédération Internationale de Football Association’s (FIFA) tight requirements. The system records via multiple cameras, allowing referees to analyze the controversial moments, frame-by-frame, concurrently from several cameras; images can also be magnified. And the slow motion playback can be included in the television broadcast.

The system works by combining multichannel recording with an instant replay system that can obtain scoreboard information to create an interface for video judging. In addition, events can be marked live or recorded, making them instantly available for review. It is used in basketball, soccer, football and many other sports.

“We have a full range of sports video judging solutions, from a budget four cameras to the most powerful 24 channels system,” said CEO Michael Gilman. “We also have HD and 4K systems for broadcasts including special servers that are dedicated to super motion cameras, such as Panasonic’s 4X AK-HC5000.”

Increasing content volumes, shorter turnaround times, and a growing range of outlets for content are combining to drive media enterprises to look for ways to automate their production workflows. One technology that has begun to bear fruit is Artificial Intelligence, which can be used to streamline repetitive tasks and simplify media operations.

For an in-depth analysis of the survey results, download the whitepaper titled "There's Nothing Artificial About A.I. for Broadcasters".

TV Technology recently completed survey of over 300 media professionals, sponsored by Quantum, that shows artificial intelligence (A.I.) technologies are already in use within the media and entertainment industry. Fully two-thirds of the companies which have tested A.I. have adopted it for one or more tasks. Large media organizations, those with media archives containing over 20,000 hours of content, are particularly enthusiastic adopters of A.I. technology for both metadata creation and automated clip generation and distribution.

Providing appropriate tags for new materials and content that is already stored in media libraries is growing in importance as all types of media organizations seek to maximize the value of content they already own. Quantum Corporation has developed an e-book titled “Applying the Power of AI to Your Video Production Storage” that describes how metadata can be automatically created using on-premises appliances to analyze content housed in object storage. This e-book also explains how to avoid the cost, complexity and potential security risks of uploading content to a public cloud service.

Quantum offers perspective on the value of metadata for locating and using existing video and audio files in a blog post titled “It’s 2020—Why Aren’t You Mining Metadata?” Many media companies have large and growing archives of raw and fully-produced content that can be re-purposed to generate new revenue streams. To unlock the value within these files, metadata is crucial, providing the means to locate clips that are relevant to current events or ones that can be used in new productions. This blog post describes some of the key ways in which content companies can increase their revenues and viewer mindshare by leveraging assets they already own.

At NAB 2017, Quantum showed a new A.I. system in operation that was specifically designed to process large volumes of video contained in object storage owned or operated by media companies. The aiWARE for Xcellis™ product that was demonstrated is described in detail on this web page which also includes links to the product data sheet and other useful reference material.

Additional Resources:

Ebook: Applying the Power of AI to Your Video production Storage

Find out what AI means for the media and entertainment industry, How AI can help your video production workflow and the value of running AI engines on an on-premise appliance. Click here for more.

Webinar: The Future of Artificial Intelligence in Broadcasting

Join TV Technology as they reveal results of a survey on how the M&E industry is deploying A.I. today. Sponsored by Quantum, the survey revealed that two-thirds of the organizations that have tested A.I., have deployed the technology for at least one critical role in their workflow. Find out the applications where A.I. is making a difference in M&E workflows and more.

Watch the webinar here.

Most broadcasters and media businesses are now on their second or third generation asset management system. What was once an enforced necessity—a means of keeping track of content on servers and data tapes—is now a positive asset, lying at the heart of the workflow.

Those workflows, in turn, draw together small blocks of functionality (tasks) with, where necessary, intelligent decision-making and branching based on metadata values. They can include manual as well as automated functionality.

The workflows can also be triggered by other systems, and report to them. The business management of an enterprise will be overseen by a system such as SAP: it makes sense for the media workflow layer to provide detailed reports and audit trails into the enterprise management layer. That way the board can take decisions and initiate new services, based on real information on how services are performing and what they cost.

Through this vertical integration the chief engineer knows that, for instance, the transcoder farm is a bottleneck; the CFO knows what impact this bottleneck is having on revenues and what the cost of relieving it will be.

The successful platform, therefore, will require constant enrichment of the metadata. Some of this information will come from the content: in the DPP/AS11 wrapper if it is bought-in content; or as shooting data if you are in production. There are the familiar metadata categories, which are added as you process it: things like reference numbers, program titles, precise durations, compliance edits and so on.

But you can enrich the metadata in many more ways. The idea of tagging—attaching descriptive metadata to a specific timecode—is extending beyond sport to other program genres. Some technical metadata is generated as it is processed through internal workflows: transcoding formats, for example.

Metadata can be enriched through queries of external data sources. If you buy in a movie, you could harvest further information from websites like IMDB and Rotten Tomatoes. If you are covering a golf tournament, you could collect course statistics from the club’s website and player rankings from the PGA.

If your application needs it, scripts can be transcribed, either by an external service or using voice to text conversion. As well as providing additional internal functionality, this continuing enrichment of the metadata can feed on to the delivery system, providing all the information needed for subscribers to discover and explore content, for instance.

It is tempting to think of metadata as a single collection of information, but in our software-defined architecture, as well as moving content between computers, there is no reason why we should not share metadata between systems. The playout software, for instance, needs a set of key information but it does not need access to all the metadata, which is why it is common to develop simple transfer protocols between asset management and playout.

Intellectual property rights management is an immensely complex subject, and its users tend to maintain very sophisticated systems to ensure they comply precisely. But some information from it is valuable to other users, and the actions of other users are vital to the rights management team. As we have already seen, audit trails could be reported to the enterprise management system.

Service-oriented, software-defined workflows are without doubt the way of the future. They provide more efficient ways of working through extensive automation, better management and more responsive customer service. The key to success is metadata.

The workflow orchestration layer is the metadata engine, which is asset management. It must interface with external systems, whether that is by secure link to the enterprise management infrastructure or harvesting information from multiple relevant websites. The asset management platform stores much of our metadata, and knows where to get the rest.

The key to getting the true benefits of the move to IT-centric broadcast and media infrastructures lies in developing new, service-oriented ways of working, allowing the organization to determine what best suits its creative and commercial needs, freed from the limitations of fixed architectures. That cannot happen without the right metadata.

This story first appeared on TVT's sister publication TVB Europe. 

BAYSIDE, CALIF.—Content delivery network and streaming media provider StreamGuys has come out with a new SaaS-based metadata delivery system, the SGmetadata. The new system is designed to reduce the informational workload associated with connecting and updating listeners to streaming media.

SGmetadata employs a push-based metadata system that sends the full layer of the provider’s HTTP protocol, including headers and extras, to the user just once at the beginning of the streaming session. Additional metadata is only sent then when required. The SGmetadata system can also serve out metadata to StreamGuys’ SGplayer or to any other streaming platform and browser using web socket software like Socket.io.

The system is backwards compatible with older browsers and streaming players and simplifies connectivity with third-party service providers. Additionally, SGmetadata can be integrated with StreamGuys’ SGalerts software, which allows streaming providers to be automatically informed when metadata streams are not changing in sync with automation-controlled schedules or have completely stopped working. 

NEW YORK—As NBC’s iconic “Saturday Night Live” approached its 40th anniversary earlier this year, the idea of creating an app came up. But, says Michael Martin, senior vice president, product, technology & operations at NBC Entertainment Digital, they had to think long and hard about whether to proceed. “Consumers are much more discerning and careful as to whether they want to go through the steps of downloading an app and taking up phone storage,” he says. “The bar is pretty high nowadays.” Discoverability is also an issue; experts estimate there are more than 3 million total apps for Apple and Android.

A successful app, Martin and his group decided, had to fulfill two criteria: It had to perform a utility really well and communicate that utility easily, and the assets had to be unique enough to merit its own app. Forty years of SNL has produced 800 episodes with roughly 6,000 individual comedy sketches. But the library had already been digitally available for some time, and Martin was troubled at how viewers consumed the content. “Fundamentally people were snacking on that content,” he said. “The consumption would be to watch one or less than one clip of the newest, latest, most topical video.”

Michael Martin, senior vice president, product, technology & operations at NBC Entertainment Digital

A MYRIAD OF DESCRIPTIONS
How to make every season and every sketch more discoverable and valuable was the challenge. One advantage the digital group had was NBC’s 40 years of audience research. The first important piece of information gleaned was just how much more varied the sketches were, by season, cast members, guest stars, topics, categories and many other factors. “We realized that there were scores, if not hundreds of facets that could describe an individual sketch,” he says. “We needed something different than the typical recommendation engines, based on commonality in consumption patterns. Those patterns didn’t hold for this content.”

They hired what they called a “metadata army” of people who took four months to go through all 6,000 sketches, recataloguing them for the hundreds of defined facets. “The semantic model is very flexible,” said Martin. “It isn’t just the facets that are important but their relationship to one another.” The sketches were ultimately connected by 33,000 relationships. “It became an incredibly dense model, so we can take some of what we learned from audience research and put a prediction engine on top of it,” he adds.

Eddie Lee, vice president of technology for NBC Entertainment Digital, was responsible for building the prediction engine from scratch. “We tried to apply and re-use some of the traditional, off-the-shelf recommendation engines, but they couldn’t deal with our model,” Lee says.

The app and website—powered by the MarkLogic Enterprise NoSQL database platform— integrate, manage and search the database. “Building the prediction engine took two to three months, constructing and iterating it,” he said. “It included connection between the CMS [content management system] and the database, built on top of MarkLogic using our resources and our set of rules.” The system also has enterprise traits for security, data integrity, and scalability.

More than 100 million clips have been streamed since the SNL Official App was released in February.

SWIPING FOR APPROVAL
Also critical was an interface mechanism that let users interact with the content, and, the team hoped, turn a snack into a meal. The User Experience team came up with a gesture-based function that provided real- time feedback to the engine. “We didn’t want to give the user a choice of thumbs up or thumbs down,” Martin said. “The user wouldn’t just perceive that as work but it’s too simplistic to get the outcome we were looking for.”
Instead, if the viewer swipes left, he or she gets a new video. “If you’ve watched 75 percent of that first video, we assume that you liked most of it and will harvest facets that we’ll serve in the second and third video,” says Martin. “If you swipe it very quickly, we assume that you didn’t like that one whatsoever and revise the model accordingly. And if you swipe five or six videos very quickly, we’ll put you on another path, in a totally non-technical way.”

Swiping up and down to add to favorites is another way that the app signals viewer preferences about the sketches. “We built that interface into a product in a way that we don’t have to spend any time up front to explain it to the consumer,” said Martin. “But it also results in a high-quality data stream.” He also notes that the data is completely anonymous. The UE design was recently nominated for an Interactive Emmy for outstanding user interface.

The response to the app has been stronger than Martin and his team could have anticipated. “We had over 100 million clips streamed to the app,” he said. “Most critical, we definitely unlocked binging. On average, people who were consuming less than one sketch in the past are now watching several to a dozen sketches.”

Since its February release, the team has continually updated the app and is now at version 1.3.1. “We’ve extended capabilities and added new features and polished some things to make predictions better,” Martin said. And the team has also prepared some entirely new features for the opening of SNL’s 41st season, Oct. 3. “We are adding new social features so people can share their personalized experiences with family and friends,” Martin added. “We’re also looking for the opportunity to get the cast and show more involved in curating and highlighting content. And our emoji library will expand to include new cast members.”

The app is free and available on iOS and Android (www.nbc.com/saturday-nightlive/app).

Roger Franklin

LONDON —The TV landscape has changed dramatically in recent years, with consumers growing accustomed to receiving content they want when and where they want it. With this evolution is increased pressure on video providers to know what the customer wants and to provide recommendations and compelling value added services. Providers are tapping into metadata to increase service value and generate new revenue and business models.

Metadata has been present in video streams since the middle of the 1970s. It is in the changing environment of consumer-driven content consumption that metadata is the way to enhance the viewing experience.

SCTE-35 STANDARD

With the SCTE-35 standard becoming a requirement, metadata must be handled in a very specific way. There is some confusion as to what adhering to the standard actually involves.

The SCTE-35 standard provides more flexibility than previous methods, making it possible to fully describe a segment within a program. SCTE-35 messages not only define the start and stop points of a segment, but also the duration, and the content within the segment. This flexibility makes it easier to provide personalized content.

REQUIRED METADATA

Two primary pieces of metadata are required within the video stream: the first identifies where in the stream a segment starts and ends; the second identifies what content is present in that segment.

In order to set those parameters, the video provider must determine what constitutes a segment for them; e.g. the main program is often one segment, with other segments for national and local advertising spots.

SCTE-35 can be used to define more specific segments than that. Individual scenes can be defined, enabling the possibility of scene substitution based on the age and preference of the viewer.

The rules for content replacement are defined outside of the video stream and should follow the Event Signalling and Management (ESAM) and Event Scheduling and Notification Interface (ESNI) standards.

BEING SCTE-35 COMPLIANT

SCTE-35 splice messages indicate where to replace content in a video stream. Splice points must be identified at the beginning and end of the content segment. Every splice point should begins with and encoder-created IDR frame and the encoder should insert the SCTE-35 splice message. The splice commands are contained within a splice information section SCTE-35 message. 

Because duration is present, while it is good practice to supply both the start and stop points, it is only a requirement to provide one.

SCTE-35 messages are used to define what content is in each segment. Splice descriptions identifying the content in each segment can be added to a splice information section message. The SCTE-35 standard predefines three types of descriptions: the Avail descriptor is added to the splice insert command, and this descriptor functions like a cue tone. It is interpreted by the receiving equipment as a splice point for substituting local advertising into the program stream.

The DTMF descriptor instructs a receiver to initiate the specified DTMF tone. The result of this descriptor is the same as the Avail, but it accomplishes this by signaling older splicing equipment that still require the audible DTMF tones.

The segmentation descriptor is used to define the content of a program segment. This descriptor has fields that are used to turn web distribution on or off, or mark whether a segment should or should not be blacked out.

According to the specification, the segmentation descriptor is added to a time signal command or other splice commands, and is used to identify the content of a new upcoming segment. The segmentation descriptor message should be delivered at least four seconds before the beginning of the segment, but is often delivered eight to 12 seconds before the segment.

MAKING THE MOST OF METADATA

With the pace of change in the video distribution industry, it is clear the future for content distributors is in the value added to the distributed content. It is a challenge to implement new standards quickly, especially when you need to leverage older equipment and systems that are not fully compliant. However, the companies that plan today to manage content smartly will reap the rewards, delivering compelling content to viewers, and generating new revenue streams.

SEATTLE – White-label video publishing company thePlatform has announced the cloud-based Unified Ingest Service to centrally manage all video and related metadata files is now available for operators who want to publish to subscribers’ set-top boxes and other IP-connected devices.

The Unified Ingest Service is a streamlined system that manages direct feeds from satellite transmissions, traditional video pumps, operator-owned content delivery networks and external CDNS for online video sources. It also provides centralized back-end resources to package, promote and sell video services.

It can ingest both video files and metadata. For video files, the system supports the industry’s shift to terrestrial delivery and on receiving one HD mezzanine file for both traditional cable delivery and IP delivery; provides advanced workflows to get files into formats needed for legacy STBs, next-gen STBs, game consoles, tablets, smartphones and more; and works with thePlatform’s mpx Accelerate service.

Metadata ingest centralizes and normalizes metadata from multiple soruces, including TV listings and program descriptions from metatdata providers like Rovi, Gracenote and Red Bee; episode and movie descriptions from both TV VOD Asset Distribution Interface ingest and online video sources; and complete cast and crew lists from IMDB or directly from networks and studios.

The Unified Ingest Service is available as a standalone service for pay TV operators, or as part of a broader suite of video publishing services from thePlatform’s mpx system.