Residual in Residual Based Convolutional Neural Network Inloop 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 inloop 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 inloop filter to replace conventional rulebased 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 inloop filtering. In addition, we build a large scale database for the learning based inloop filtering algorithm. Experimental results show that our method achieves on average 7.5%, 16.9% and 18.6% BDrate 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% BDrate reduction separately.

A CNNbased Inloop Filtering Approach for AV1 Video Codec Inloop filter using Convolutional Neural Network (CNN) has lately attracted lots of attention in video coding. CNN models may be trained to learn how to restore degradation introduced by compression in pictures, and hence effectively help improve the coding efficiency. Stateoftheart work in this field generally employs a single network to enhance reconstructed frames mainly in intra coding. In this paper, we develop a depthvariable network handling both intra and inter coding. The depth of our network is varied with the distortion levels of reconstructed frames. Moreover, we leverage a skip enhancing strategy for inter coding, which improves both the coding efficiency and the resulting visual quality, while maintaining low computational complexity. We apply our approach to AV1, a newly released video coding standard from AOM. Experimental results show that our approach achieves an average BDrate reduction of 7.27% and 5.57% for intra and inter modes, respectively, compared to AV1 anchor. The code and model of our approach are published in our Github website 1.