Video Coding with Cross-Component Sample Offset

TL;DR

This paper introduces a novel cross-component sample offset (CCSO) in-loop filtering method that leverages correlations between luma and chroma components to improve video coding efficiency and visual quality, validated through experimental results.

Contribution

The paper presents a new CCSO in-loop filtering technique that enhances coding efficiency by exploiting cross-component correlations, implemented with a look-up-table and adopted into a next-generation video codec.

Findings

Achieves up to 3.5% PSNR gain in video coding.

Improves VMAF quality metric by 1.8%.

Enhances subjective visual quality.

Abstract

Beyond the exploration of traditional spatial, temporal and subjective visual signal redundancy in image and video compression, recent research has focused on leveraging cross-color component redundancy to enhance coding efficiency. Cross-component coding approaches are motivated by the statistical correlations among different color components, such as those in the Y'CbCr color space, where luma (Y) color component typically exhibits finer details than chroma (Cb/Cr) color components. Inspired by previous cross-component coding algorithms, this paper introduces a novel in-loop filtering approach named Cross-Component Sample Offset (CCSO). CCSO utilizes co-located and neighboring luma samples to generate correction signals for both luma and chroma reconstructed samples. It is a multiplication-free, non-linear mapping process implemented using a look-up-table. The input to the mapping is a group of reconstructed luma samples, and the output is an offset value applied on the center luma or co-located chroma sample. Experimental results demonstrate that the proposed CCSO can be applied to both image and video coding, resulting in improved coding efficiency and visual quality. The method has been adopted into an experimental next-generation video codec beyond AV1 developed by the Alliance for Open Media (AOMedia), achieving significant objective coding gains up to 3.5\,\% and 1.8\,\% for PSNR and VMAF quality metrics, respectively, under random access configuration. Additionally, CCSO notably improves the subjective visual quality.