Metadata Is Key To Unlocking AI’s Potential

Artificial Intelligence (AI) – which we should all really be calling Machine Learning - has found many applications within the media & entertainment world, driving innovation and pushing the boundaries of video production technology and advanced workflows. There’s a little secret about AI applications in media: metadata makes them work.

Broadcasters were early adopters of AI, dating back to 2018 when the first AI news presenter went live on air. China's state-run Xinhua News Agency launched an AI anchor called “English AI Anchor,” which debuted at the World Internet Conference. We have come a long way since, as broadcasters continue to leverage the technology to streamline workflows and drive growth.

A sports fan watching their favorite team on TV is probably unaware of the impact of AI, but the truth is that the immersive (and interactive) sports broadcasting experience today would not be possible without the power of metadata.

Real-time auto-generated metadata in sports broadcasting is becoming a standard practice, enabling broadcasters to generate significantly more metadata from their sports content than manual tagging. Metadata tagging is crucial to content discoverability, user recommendations and cataloguing, as this data links a particular event to a clip store within a vast content library.

For example, during a football match telecast, AI engines can identify scenes within the match, such as goals, fouls, and celebrations. They analyze the keywords present, such as player names, team names, and stadium names. They can also identify brands visible on players’ jerseys, logos displayed on stadium billboards, famous faces in the crowd, and even product mentions during interviews or sponsorships.

This data is then tagged into the clips as metadata. If it weren’t for AI, these data points would be lost in the vast content library and the discoverability of these clips would be significantly reduced.

AI is also being used for IP network management to ensure smooth and efficient delivery of live sports content to viewers. Likewise, OTT platforms and broadcasters use AI systems to optimize network bandwidth usage while delivering high-definition video to users.

During the game, the AI system analyzes the scene quality in real time by examining individual video frames. It can recognize various factors like motion, clarity, and detail to determine the quality of the scene. If the AI system detects poor scene quality, such as blurred images or pixelation, it alerts the system’s encoders to increase the speed at which the video is encoded and transmitted.

On the other hand, if the AI system recognizes that the scene quality is already high, it allows the network to use the lowest bandwidth possible while maintaining the necessary video quality. This optimization ensures that the OTT platform or broadcaster does not have to allocate more bandwidth than required, thus conserving network resources and reducing costs.

The use of AI for IP network management - via metadata - in sports broadcasting becomes particularly important when delivering content over mobile networks. By optimizing the bandwidth usage, the AI system supported by metadata enables users to experience good image quality even at lower bitrates, which is key to reliable smooth streaming and an enjoyable viewing experience on mobile devices.

The use of metadata that makes AI work in sports broadcasting has already made significant strides, revolutionizing the industry and enhancing the way fans engage with sports content. However, the future holds even more exciting possibilities for AI in this domain.

Looking ahead, we can expect metadata-assisted AI to further refine and personalize the sports viewing experience. As AI algorithms continue to analyze vast amounts of user data, recommendations will become increasingly accurate and tailored to individual preferences. AI will not only suggest matches and events but also offer deeper insights, player statistics, and real-time analysis specific to each viewer’s favorite teams or athletes.

Furthermore, advancements in virtual reality and augmented reality technologies will create immersive and interactive experiences for sports fans. AI-powered virtual stadiums, where viewers can virtually attend games and explore different camera angles, will become feasible. This could allow fans to feel like they are part of the action, enhancing their emotional connection to the sport.

AI is also likely to play a crucial role in enhancing the production and distribution of sports content. Automated camera systems, powered by AI, could help capture the best angles and moments in real time, minimizing the need for manual camera operations. AI algorithms could seamlessly edit and package highlights, ensuring rapid delivery of captivating moments to viewers. In truth, AI is just getting started and will continue to enrich the experience of sports fans.

At the end of the day high-quality and accurate data ensures that the AI model can make accurate and reliable predictions. It's not just about quantity, it's also about having the right data. This data must be accompanied by relevant metadata, which provides context and makes the data understandable and usable.

Metadata, then, is information that brings context to other data and is generated in each phase of the machine-learning lifecycle. All ML-related processes create specific metadata, from data extraction to the model monitoring phase. This metadata allows media organizations to produce organized, relevant, and visible content.

In today’s immersive viewing world, broadcasters and video production need to know how both AI and metadata work and incorporate them in their production workflows in order to help maintain their competitive advantage. The powerful combination of artificial intelligence and metadata is poised to change the future of content production.

Given the efficiency and positive customer experience that AI and metadata bring, users stand to increase not only engagement and brand visibility but also revenue.

You might also like...

Designing IP Broadcast Systems: Part 3 - Designing For Everyday Operation

Welcome to the third part of ‘Designing IP Broadcast Systems’ - a major 18 article exploration of the technology needed to create practical IP based broadcast production systems. Part 3 discusses some of the key challenges of designing network systems to support eve…

What Are The Long-Term Implications Of AI For Broadcast?

We’ve all witnessed its phenomenal growth recently. The question is: how do we manage the process of adopting and adjusting to AI in the broadcasting industry? This article is more about our approach than specific examples of AI integration;…

Next-Gen 5G Contribution: Part 2 - MEC & The Disruptive Potential Of 5G

The migration of the core network functionality of 5G to virtualized or cloud-native infrastructure opens up new capabilities like MEC which have the potential to disrupt current approaches to remote production contribution networks.

Standards: Part 8 - Standards For Designing & Building DAM Workflows

This article is all about content/asset management systems and their workflow. Most broadcasters will invest in a proprietary vendor solution. This article is designed to foster a better understanding of how such systems work, and offers some alternate thinking…

Designing IP Broadcast Systems: Addressing & Packet Delivery

How layer-3 and layer-2 addresses work together to deliver data link layer packets and frames across networks to improve efficiency and reduce congestion.