It happens all too often. A high-profile live broadcast is in progress, millions of viewers are tuned in, and suddenly—buffering. The screen freezes. Panic sets in. Engineers scramble to identify the cause, only to be met with the all-too-familiar response from network providers: “It’s not on our end.”
For years, media and broadcast companies have battled invisible network disruptions, struggling to pinpoint whether the issue stems from their infrastructure, a third-party provider, or somewhere deep within the network maze. Traditional monitoring solutions fail to provide real-time, end-to-end visibility, leaving teams playing an endless game of blame and guesswork.
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The evolution of professional audiovisual technology was once limited to organizations with significant financial and technical resources. However, rapid advancements in computational power, cloud computing, and artificial intelligence have altered this landscape. Today, sophisticated audiovisual tools are no longer restricted to large studios but are transforming industries such as media, fashion, sports, and luxury.
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As television consumption continues to evolve, service providers must embrace new technologies to remain competitive. The traditional model of content delivery, where viewers passively consume programming on rigid schedules, is long gone. Today, audiences expect seamless, personalized, and on-demand experiences that cater to their preferences, interests, and habits. At the same time, monetization strategies are becoming more sophisticated, moving beyond basic subscription models to incorporate advertising, hybrid AVOD/SVOD approaches, and real-time data-driven revenue opportunities.
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Digital transformation in the Media and Entertainment (M&E) market involves many applications that need to exchange content and metadata (data) in a fluid and efficient manner. For media companies operating in today’s digital world, producing, managing and distributing content requires an efficient and agile media supply chain, where metadata impacts all of these processes.
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In today’s digital landscape, the demand for high-quality video content is ever-growing, driving the need for robust platforms that can support the development of modern video applications and streaming media services. One of the key challenges for enterprises, broadcasters, and content creators lies in effectively managing and delivering video content across various channels and devices. The first hurdle consumers encounter when wanting to view content is being able to access it. We’ve all heard or seen about big events where people haven’t been able to sign in at the time the event begins or where the video streaming quality has suffered.
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As millions of users consume streaming video content across various platforms daily, video app providers and mobile network operators face immense pressure to manage data usage and bandwidth efficiently. This challenge presents an opportunity to make the industry more sustainable. Leveraging data analytics captured through real device testing, streaming video service providers can not only optimize data transmission but also reduce energy consumption and promote sustainable user behavior. Vodafone, Telefonica, and Meta have already communicated interesting results in this field.
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When none other than Tyler Perry halts an $800 million studio expansion after seeing a text-to-video AI demo, you know something major is happening in media and entertainment. AI isn’t new to the industry—Netflix has used machine learning (ML) to serve up recommendations since the early 2000s—but generative artificial intelligence (GenAI) is changing more than distribution and marketing. GenAI is primed to change how film, television, and music are imagined and produced.
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Financing Movie Making requires convergence of investors, bankers and several financial institutions coming together. The entire movie making process is complex across the lifecycle of pre-production, production, post-production, distribution etc.
As a producer of a movie, the intent is to ensure the success of the content (movie) and make financial profit. The entire moving making process results in a lot of data generation (from scripts, marketing assets, actors, posters, trailers, props, exchange of information, ideas and so many other aspects across the lifecycle).
Can AI or GenAI help with finding patterns through the latitude of data across the movie making lifecycle? Can it help with prediction of success of movies that allows producers, directors, financiers to take informed and wise decisions for moving making? NStarX Data Scientists have been looking at this problem for a while now!
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The media and entertainment landscape is undergoing a seismic shift. Artificial intelligence (AI) is shaping every aspect of content creation, production, and delivery, streamlining workflows, adding efficiencies and delivering better experiences for viewers. At IBC2024, the all-new AI Tech Zone in Hall 14, powered by the EBU, promises to be the place to cut through the hype and discover the impact AI can have now and in the future.
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Perifery’s Intelligent Content Engine (ICE) is a software platform that leverages AI agents and advanced AI models to manage, organize, and curate media content such as images, videos, audio files, documents, and other multimedia assets. Acting as an AI Media Content Librarian, ICE examines, understands, and catalogs every file within its view. It automatically categorizes, organizes, and understands media assets based on the content itself regardless of the existence of any traditional metadata.
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