Post Sample Adaptive Offset for Video Coding

Abstract

In-loop filtering is an important task in video coding, as it refines both the reconstructed signal for display and the pictures used for inter-prediction. At the current stage of the Versatile Video Coding (VVC) standardization, there are three in-loop filtering procedures consisting of deblocking filter (DBF), sample adaptive offset (SAO) and adaptive loop filter (ALF). Among them, SAO is the simplest in-loop filtering process and highly effective in removing coding artifacts. It simply modifies decoded samples by conditionally adding an offset value to each sample after the application of the DBF. For this, a classification is applied for each sample location, which gives a partition of the set of all sample locations. After that, an offset value is added to all samples associated with each class. Therefore, the performance of SAO essentially relies on how its classification behaves. In this paper, we introduce a novel classification method for SAO. Based on this, we derive an additional SAO filtering process which we call post sample adaptive offset (PSAO). Experimental results show the effectiveness of our proposed PSAO filtering process. On average, 0.42%, 0.31% and 0.33% additional coding gains can be achieved on top of VTM-5.0 for all intra (AI), random access (RA) and low delay with B pictures (LB) configurations, respectively.