AMSTERDAM—Qvest has introduced a new version of its clipbox studio server designed for ingest and playout with full support for the new renderless workflow the relies on the Web Render Engine (WRE) from Arvato System’s Vidispine brand at IBC 2024, which concludes here today.

The latest version of clipbox will give video editors, journalists and others in the newsroom a tighter collaborative workflow and reduced production delays, the company said.

The new version offers users greater customization flexibility for their ingest and studio playout workflows, especially in fast-turnaround news workflows where every second counts. It also provides full support for real-time rendering with transition effects, so users can play out sequences directly without requiring a video switcher, it said.

Other benefits include clipbox’s ability to create proxy files in parallel to either HD or UHD recording that can be opened and edited during ingest. These files are also fully synchronized with the full resolution assets, saving editors time otherwise spent waiting for the recording to be finalized before it can be edited.

“Customers all over the world are already using clipbox for their studio ingest and playout. It’s exciting to think about how much more efficient we’ll be able to help make their workflows with this latest version, while of course continuing to be the most cost-efficient newsroom studio server available,” said Frank Mistol, managing director of Qvest.

The renderless workflow created by the Web Render Engine was developed by Arvato Systems’ Vidispine team. It is designed to save editors time regardless of where they are. With this in place, multiple editors can work on the same content at the same time without needing to wait for any assets to render, the company said.

With clipbox supporting WRE sequences natively, a unique fast-turnaround workflow is created with no videos needing rendering in advance. Even transitions can be executed in real time. The workflow enables video clips to play out from shared memory, removing the need to copy them at the expense of time or costs. It also supports playout of sequences while content is still being recorded, which allows time delay playback, it said.

“Vidispine’s Web Render Engine revolutionizes production by allowing content to be stored and managed efficiently without creating new files for every sequence. Only metadata is generated when users edit a sequence, eliminating the need for file movement or rendering. This enables multiple editors to work simultaneously on the same content from anywhere,” said Karsten Schragmann, head of product management at Vidispine.

See Qvest at IBC 2024 Stand 10.C24.

See Vidispine at IBC 2024 Stand 7.A15.

More clipbox information is available on the Qvest website.

More Web Render Engine information is available on the Vidispine website.

AI: WHAT'S WHAT?

No other topic has dominated industry conversation in recent years like AI. But what exactly does it mean when we speak of AI? 

Artificial intelligence is the generic term for a machine simulation of human cognitive abilities. Machine Learning, in turn, describes a series of mathematical methods that can identify certain patterns in data from learned examples. Deep Learning is a subset of machine learning and uses artificial neural networks that enable the system to learn autonomously. 

DEEP LEARNING IN MEDIA

Deep Learning enables the processing of amounts of data that is not practical to process manually. The strength of deep learning lies in capturing patterns and structures of different data types, as well as in tagging and enriching data. With its daily flow of current facts, figures and data, the media sector is ideal for the application of deep learning. 

Although many media professionals are skeptical about AI, recent studies find they would be comfortable with AI-generated news like traffic or weather reports. But without the right strategy, more automation can quickly become a nightmare.

AI IN THE MEDIA SUPPLY CHAIN

How do we integrate our existing systems with the rapidly growing field of AI providers with pre-trained models, frameworks and environments ready to be used as services?

First, we need to look at where we might apply them. There are opportunities throughout the media supply chain. A few examples include: 

• Ingest—Automatic QC, compliance, deep fake recognition, copyright monitoring  
• Production—Tagging, entity recognition, topic clustering and (soon) rough cuts, automatic highlight cuts, robot journalism 
• Planning—Automatic program planning, based on licensing or marketing patterns 
• Marketing—Rating prediction, imitation of buying patterns 
• Distribution—Automated playout or packaging

A GOOD STRATEGY

Deep Learning helps us to gain insights into media objects at a level that wasn’t practical without automation and helping us toward our vision of wanting to know "everything about every frame."

To support to the multitude of services available and bridge the data and organizational silos that segregate both content and business intelligence, we implement an “AI-specific” intelligence layer that manages all communication, but also adds value through:

• Normalization—Bringing results into a unified format 
• Cross-media analysis—Video, stills, audio, text 
• Multicloud—Connect many different providers 
• Training—Especially in the field of computer vision 
• Knowledge graph—Build contextual data models from different data silos and query them in real time with dynamic requests  

Supporting a "best-of-breed" approach, users can choose the combination of services that best fit their requirements. This is realized through the normalization of different result schemes and making them available in a uniform metadata model. In this way, a uniform experience is achieved without neglecting the special knowledge or features of the individual services.

Applying a uniform metadata also has further advantages. Recognition concepts analyzed by different services can be merged, compared and interchanged. We can also combine services, for example, a speech-to-text transcript from one operation can be sent through Natural Language Processing in a “Cascade” operation.

A standardized metadata set and version tracking enable us to reproduce individual results ourselves and also determine where the data actually comes from and what predicted confidence was recognized. This enables users to rapidly optimize—for example changing threshold values—with results displayed immediately without having to re-analyze all media.

Organizations using deep learning need to train the algorithms with the data that is appropriate to their needs, and continuously train as those needs evolve—especially important in dynamic environments such as news where topics/people/objects of interest constantly change. Creating labeled training data is the “tagging” of tomorrow but being not the primary task in a creative process, the effort for this should be minimized. Since the training data is media objects, why not do this directly in the MAM with easy tools for media managers or journalists, integrated into daily tasks.

HOW DOES AI ENTER INTO PRODUCTION?

"AI" is an interdisciplinary team sport—from idea to validation by means of a prototype, up to the transfer into production, many different roles are required, including:  

• Business analyst—The domain expert  
• Data engineer—Provides data sources in sufficient quantity and quality 
• Data scientist—Implements and verifies the algorithms

The 80/20 rule applies here with practical experience showing that data engineering often takes up the majority of the work, whereas implementation accounts for a smaller part.

With roles defined, it is recommended to take a standardized “go live” process as follows:

• AI Roadmap—Identify & prioritize relevant use cases  
• AI Lab—From idea to a verified prototype within a few days 
• AI Factory—Develop operational AI service fully integrated to the production environment 
• AI Operation—A stable and permanent operation and ongoing improvement 

As a global leader in the world of IT, AI has been an important topic within Vidispine and the Arvato Systems group as a whole. We have fostered professional and creative exchange in the Arvato Systems AI Competence Cluster—a network of interdisciplinary colleagues aiming at transferring knowledge and driving innovation. To this end, many interesting examples from other businesses are coming to the forefront, such as interactive fashion recognition, extraction of manuscript insights, anomaly detection of infrastructure, data journalism (e.g. through a “crime map”), to name a few. It is clear that in the future, AI will simply be a part of every IT toolbox.

Ralf Jansen is software architect and product manager, Vidispine, an Arvato Systems brand

GÜTERSLOH, Germany—Arvato Systems has announced its plan to combine its media & entertainment product portfolio with the Vidispine businesses under a relaunched Vidispine brand.

Arvato acquired Vidispine, a media supply chain company, in 2017 as part of a strategy to strengthen the link between the two companies’ complimentary solutions and expand into new markets.

This new Vidispine brand is meant to be a “content ecosystem” for Arvato’s product portfolio and professional services with a community of partner vendors, consultants and service providers and developers, according to the announcement.

The restructured portfolio includes media asset management, content planning & rights management, ad tech and the cross-industry content platform, VidiNet.

“In order to better support our customers, we feel the time is now right to combine these two leading portfolios under the Vidispine brand,” said Ralf Schürmann, CEO or Arvato systems. “Streamlining our portfolio and creating the framework for the content ecosystem enables us to better help companies working with media to focus on their core business, by providing easy access to technology that supports their business needs. The Vidispine platform enables customers to get the maximum value from their assets, rights, media inventory and market.”

For more information, visit www.vidispine.com. 

GÜTERSLOH, Germany—Arvato Systems and Vidispine have announced that they will not be attending IBC 2020 in Amsterdam, which is currently scheduled for Sept. 11-15.

In the announcement, there is no specific mention of coronavirus as the reason for withdrawal, although the pandemic was likely a key factor in the decision. COVID-19 has already caused the cancellation of the physical 2020 NAB Show and the postponement of other conferences.

“As a major milestone in our event calendar, this has been a difficult decision to make,” said Ralf Schürmann, CEO of Arvato Systems S4M GmbH. “Our focus is now on ensuring that our customers can maintain business continuity during these challenging times. And of course, we are considering how we can keep them fully informed and supported with the latest solutions and innovations from Arvato Systems and Vidispine.”

As a substitute for not attending IBC, Arvato Systems has announced that it is hosting online events and is continuously looking to expand its program of virtual user group meetings, as well as other ways of staying connected with customers and partners, the company says.

One such event will be an online technology summit, “vortechConnect.by/Vidispine,” set to take place on May 27. For more information on the summit, visit www.vortech.by/connect.  

GUTERSLOH, Germany—Arvato Systems and its Vidispine business have added advanced cognitive functions to Vidinet that enable its users to execute multiple media analyzers and pre-trained AI models via a single interface to unify, aggregate and normalize results.

The functions are available to users of the Arvato Systems Video Production Management Suite (VPMS) and Broadcast Management Suite (BMS), as well as all Vidispine API-as-a-Service subscribers, the company said.

Vidinet’s unified data model, also known as the Analyzed Data Unit (ADU), is different from simple AI aggregators in the flexibility it brings to cognitive functionality, the company said.

Cascading and combining different services to gain further insight into media is supported in Vidinet. One example of how to take advantage of this flexibility is creating metadata relating to the context and tone of content. Using a speech-to-text service can create a transcript of a clip, which can then be passed through a natural language processing algorithm for tone and context. Leveraging an object recognition analysis increases the level of confidence about that data, it said.

The company plans to unveil other functionality later in the year. Vidinet will provide a common training interface for supported services, making training easily integrated into media workflows.

“This addition to Vidinet takes an existing concept of aggregating AI services and extends it in a way that makes the implementation of AI and ML in media workflows far simpler and more maintainable,” said Erik Ahlin, CEO of Vidispine.

The holistic approach behind the development of the technology and applying it to the Arvato and Vidispine portfolio will enable media companies to respond quickly to changing demands to meet the demands of consumers and advertisers with greater efficiency and profits, added Holger Noske, VP Business Solutions at Arvato Systems.

More information is available on the Arvato Systems website and the Vidispine website. 

LOS ANGELES—Arvato Systems is following up on its release of the EditMate software last year with the addition of the proxy functionality. EditMate proxy is integrated into Arvato’s VPMS media asset management system, allowing users to work in Adobe Premiere Pro with lo-res streaming of central hi-res media.

The proxy offers a centralized render engine to incorporate all effects, audio, subtitles, graphics and locally produced voice-over tracks. Users are able to edit at their desktops while their work is centrally available in EditMate project. EditMate also manages project conversion from hi-res to lo-res when needed.

EditMate proxy can hold multiple hi-res clips in a central location or distributed across customer sites, with users being able to connect to it via the internet. In addition, EditMate proxy users can work with media from parked projects or archived clips before they have been completed or restored. Users can continue editing while a project is parked and switch at any later point to a hi-res version.

Arvato Systems is located at booth 112 during SMPTE 2016, which is currently underway.

Rob Fox

GÜTERSLOH, GERMANY & NEW YORK – In an effort to build on its U.S. presence, Arvato Systems has announced that Rob Fox has been named Vice President of Media for Arvato Systems North America. Fox will develop a sales, delivery and service unit to serve the U.S. market, with a particular focus on digital media management products, including Arvato’s VPMS MAM product.

Most recently, Fox was in charge of the broadcast markets for DVS server and storage products from Rohde & Schwarz. Fox also worked at CNN for 10 years, where he served as director of operations for the financial network, CNNfn, and director of media operations at the New York broadcast studios. He also worked for Avid as manager of network operations, and ran his own video consulting and project services company.

Fox will work out of Arvato’s New York office and report to Frank Zimmermann, CEO at Arvator Systems North America.