In the video compression industry, everyone wants to encode videos with as high a quality as possible and as low a bandwidth as possible. 

The next generation of video compression technology seeks to apply artificial intelligence (AI) to the video encoding problem by implementing content-adaptive encoding (CAE), automatically sensing how “difficult” each video is to encode and adapting the encoding accordingly to ensure high quality for complex videos and maximum bandwidth savings for simple videos. 

What we have found, however, is that while it is easy to put together a CAE solution (as evidenced by the dozens of products now on the market), it is difficult to derive a CAE solution that works well. 

In this webinar, we first review some of the basics of CAE, including what types of adaptation are possible and what components are required in any CAE algorithm.  We then present Rithm, EuclidIQ’s perceptually optimal CAE solution, and explain what differentiates Rithm from other CAE solutions on the market. 

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