Residual in Residual Based Convolutional Neural Network In-loop Filter for AVS3

Deep learning based video coding tools development has been an emerging topic recently. In this paper, we propose a novel deep convolutional neural network (CNN) based in-loop filter algorithm for the third generation of Audio Video Coding Standard (AVS3). Specifically, we first introduce a residual block based CNN model with global identity connection for the luminance in-loop filter to replace conventional rule-based algorithms in AVS3. Subsequently, the reconstructed luminance channel is deployed as textural and structural guidance for chrominance filtering. The corresponding syntax elements are also designed for the CNN based in-loop filtering. In addition, we build a large scale database for the learning based in-loop filtering algorithm. Experimental results show that our method achieves on average 7.5%, 16.9% and 18.6% BD-rate reduction under all intra (AI) configuration on common test sequences. In particular, the performance for 4K videos is 6.4%, 15.5% and 17.5% respectively. Moreover, under random access (RA) configuration, the proposed method brings 3.3%, 14.4%, and 13.6% BD-rate reduction separately.