Random Forest Based Fast CU Partition for VVC Intra Coding

Abstract

Versatile Video Coding (VVC) significantly improves the coding efficiency over the preceding high efficiency video coding (HEVC) standard, but at the expense of much higher computational complexity. Specifically for intra coding of VVC, the computational burden is mainly on the brute-force recursive rate-distortion optimization (RDO) search of quadtree with nested multi-type tree (QTMT) based coding unit (CU) partition structure. Consequently, we propose a random forest based algorithm to reduce the complexity of CU partition. The CUs are first divided into three categories, namely simple, fuzzy, and complex CUs. For simple and complex CUs, one random forest classifier is trained to directly predict the optimal partition mode. For fuzzy CUs, another random forest is trained to predict whether the partition process is terminated or not. The experimental results show that the complexity reduction of the proposed algorithm is up to 69% as compared to the VVC reference software (VTM 7.0), and averagely 57% encoding time saving is achieved with 1.21 % BDBR increase.