How Deep Learning Could Revolutionize Broadcasting

How Deep Learning Could Revolutionize Broadcasting

In TechRadar, Max Kalmykov, VP of Media and Entertainment at DataArt, explores the impact of deep learning technology on video generation, restoration, editing, streaming, and analysis.

“Consumer-grade, easy to use solutions such as Flo allow you to use deep learning to automatically create a video by describing what you want in it. The software will find the relevant videos from your library and edit them together automatically. Google has a neural network that can automatically separate the foreground and background of a video.”

“The process of colorizing black and white footage has always been lengthy. There are thousands of frames of footage in a movie and coloring each one takes a long time. Even with advanced tools, the process can only be automated so much. Thanks to Nvidia, deep learning can now speed up the process significantly, with tools that only require an artist to color one frame of a scene. From there, the deep learning network automatically handles the rest.”

“The technology is not limited to detecting just faces though, sports broadcasts rely on camera people to track the movements of the ball, or to identify other key elements to the game, such as the goal. Using object recognition, AI-powered tools can be used to automate the production of a sports broadcast.”

“While Flo can identify what a scene is about and use that data to generate a video about whatever you want, that same technology can be used to sort and classify videos to make it easy to find a particular piece of footage by simply searching for people or actions that appear in it.” “Thanks to neural networks that can recreate high definition frames from a low definition input, we could soon be streaming low definition streams over our internet connection, while still enjoying the high definition glory that our displays are capable of.”

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