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.

Deep Learning Based InLoop Filter for Video Coding In lossy video coding, inloop filter can improve the image reconstruction by reducing compression artifacts and distortions. Recently, several CNNbased inloop filtering algorithms are proposed to improve HEVC. However, something deficient is still limiting their capability to further boost the coding efficiency. Specifically, things like prediction residuals and codingunit boundaries, which is quite relevant to the compression artifacts, were usually neglected; moreover, the loss functions adopted in these methods usually havenxe2x80x99t been regularized properly. To improve these inadequacies, we propose a deep learning based inloop filter for HEVC to improve its ratedistortion performance. Firstly, prediction residuals, the compression of which is actually where the encoding noise directly results from, are skillfully considered to help filtering. Then, we unify a blockbased measurementreconstruction process and a neighborhoodbased filtering process into one endtoend allconvolutional architecture to catch the relevance between compression noise and the pixelxe2x80x99s related position to the coding block boundaries. Finally, for network training, totalvariation model is adopted to derive the loss function by MAP (maximum a posteriori) estimation for better regularization. Experimentally, our proposed inloop filtering method brings average 6.5% and 4.3% BDrate reduction under AI and RA configuration respectively.