CNN-based driving of block partitioning for intra slices encoding
This work addresses video compression efficiency for next-generation encoders, offering incremental improvements in speed and coding gains.
This paper tackles the problem of optimizing block partitioning in intra slices for video encoding by using a Convolutional Neural Network-based method to replace heuristics, achieving a speed-up of ×2 without BD-rate loss or above ×4 with less than 1% BD-rate loss.
This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of $\times 2$ is obtained without BD-rate loss, or a speed-up above $\times 4$ with a loss below 1\% in BD-rate.