Learning-Based Complexity Reduction Scheme for VVC Intra-Frame Prediction

Abstract

This paper presents a learning-based complexity reduction scheme for Versatile Video Coding (VVC) intra-frame prediction. VVC introduces several novel coding tools to improve the coding efficiency of the intra-frame prediction at the cost of a high computational effort. Thus, we developed an efficient complexity reduction scheme composed of three solutions based on machine learning and statistical analysis to reduce the number of intra prediction modes evaluated in the costly Rate-Distortion Optimization (RDO) process. Experimental results demonstrated that the proposed solution provides 18.32% encoding timesaving with a negligible impact on the coding efficiency.