Learned rate-distortion cost prediction for ultrafast screen content intra coding

Abstract

As online collaborations become more prevalent, screen content has become increasingly important in real-time video communications. To reduce communication costs, the H.265/HEVC standard introduced the Screen Content Coding (SCC) extension, which achieves significant bits savings but comes with a higher encoding complexity. There is a need for ultrafast SCC encoding to meet the demands of real-time applications. Our key idea is to predict the rate-distortion (RD) cost of each possible coding unit under each possible mode, rather than performing actual coding to obtain the RD cost. Specifically, we construct neural networks to predict RD costs for intra prediction, palette, and normal intra block copy (IBC) modes. For IBC merge mode, we conduct motion compensation trials and use a linear regression network for prediction. Using the predicted RD costs, we create a partition-mode map set that …