Enhanced Cross Component Sample Adaptive Offset for AVS3

Abstract

Cross-component prediction has great potential for removing the redundancy of multi-components. Recently, cross-component sample adaptive offset (CCSAO) was adopted in the third generation of Audio Video coding Standard (AVS3), which utilizes the intensities of co-located luma samples to determine the offsets of chroma sample filters. However, the frame-level based offset is rough for various content, and the edge information of classified samples is ignored. In this paper, we propose an enhanced CCSAO (ECCSAO) method to further improve the coding performance. Firstly, four selectable 1-D directional patterns are added to make the mapping between luma and chroma components more effectively. Secondly, one four-layer quad-tree based structure is designed to improve the filtering flexibility of CCSAO. Experimental results show that the proposed approach achieves 1.51%, 2.33% and 2.68% BD-rate savings for All-Intra (AI), Random-Access (RA) and Low Delay B (LD) configurations compared to AVS3 reference software, respectively. A subset improvement of ECCSAO has been adopted by AVS3.