Textural and Directional Information Based Offset In-Loop Filtering in AVS3

Abstract

In this paper, we propose a novel low-complexity in-loop filtering approach named textural and directional information based offset (TDIO) for the emerging video coding standard AVS3 (the third generation of Audio Video-coding Standard). Different from conventional offset-based filtering methods which partially use contextual samples, the key contribution of TDIO is that it fully utilizes the textural and edge directional features of each sample to comprehensively determine which type of texture characteristics each sample belongs to. The corresponding offsets are generated and signaled to decoder such that sample-level distortion is reduced by correcting the quantization errors. Specifically, the multi-directionality and sample-intensity pattern based classifiers are first proposed to extract the directional and textural features, respectively. The classification results are obtained by incorporating these features, and the optimal offset values for each class are derived based on rate-distortion optimization. Since sample-level offset signalling may cause heavy burden to the overhead of TDIO, we subsequently propose a filtering offset sharing mechanism based on historical information between available temporal-adjacent compressed frames. In addition, an iteration-based filter adaptation method is designed to improve the local adaptivity of TDIO for better compression efficiency. Experimental results show that the proposed TDIO achieves 0.64%, 1.29%, 1.86%, and 2.20% bit rate savings for all intra, random access, low delay B, and low delay P configurations, respectively. Moreover, TDIO is helpful to improve subjective quality by leveraging the fine-grained local texture characteristics. It can be observed that the blurring and ringing artifacts could be significantly suppressed by using the proposed method, yielding higher subjective quality.