Performance Evaluation of AV1 Intra Coding Tools To meet the growing requirement of servicing highquality video contents, Alliance for Open Media (AOMedia) has recently released an opensource video coding scheme named as AOMedia Video Coding 1 (AV1). In this paper, we review its intra coding tools in details and evaluate their individual performance in OnOff tests. Our experimental results show that the additional directional mode and the recursive filtering achieve the highest YBDBR gain under all intra and random access configurations, respectively. The chroma from luma prediction saves significant bits in chroma channels. Additionally, the Paeth predictor performs betters for screen contents. Color palette prediction is seen effective for screen content sequence.

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.