Binarization and context model selection of CABAC based on the distribution of syntax element

摘要

Context-based Adaptive Binary Arithmetic Coding (CABAC) is an efficient entropy coding method in H.264/AVC, which consists of three processes: binarization, context model selection (CS) and binary arithmetic encoding (BAE). This paper proposes a new binarization and CS method to encode mb_type of depth videos. The proposed method includes 1) remapping of mb_type values based on the distribution of the mb_type; 2) classification and binarization inspired from Configurable Variable Length Code (CVLC) and 3) simple CS without using neighboring context information. Experimental results show that up to 12.55% bitrate reduction can be achieved at hierarchical B structure compared to Multi-view Video Coding (MVC) on depth videos.