Fast Mode and CU Splitting Decision for Intra Prediction in VVC SCC

Abstract

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs a quad-tree plus multi-type tree (QTMT) coding structure for Coding Unit (CU) partitioning and screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination and CU splitting early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed on average by 41.14%, with the BDBR increasing by 1.17%.