Machine Learning-Based Early Skip Decision for Intra Subpartition Prediction in VVC

The recently published video coding standard, Versatile Video Coding (VVC/H.266), has the intra subpartition (ISP) coding mode, which divides an intra-predicted block into smaller blocks called subpartitions, each of which can be predicted using the newly reconstructed subpartition while still sharing the same intra mode. It is a VVC intra prediction tool that brings significant coding gains but also increases its encoding complexity. In this context, this paper addresses how to speed up the ISP encoding process by designing an ISP early skip decision scheme using a simple LightGBM model. The proposed ISP decision expedites the encoding process by early determination of whether or not to skip the ISP mode test. The proposed method uses the mean absolute sum of transform coefficients as a key feature. Our experimental results show an average encoding time saving of 7.2% under the all intra coding configuration with 0.08% BDBR loss. Compared to the state-of-the-art methods, our solution is able to outperform related works in terms of the combined rate-distortion and time saving.