Convolutional Neural Network Based UpSampling for Depth Video Intra Coding Depth video contains depth and disparity information of a scene, which is critical for 3D video systems. In this paper, a convolutional neural network (CNN) based block upsampling method is proposed to improve the efficiency of depth video intra coding. For each largest coding tree in a depth map, it is downsampled before sent into encoder and recovered into the original size in an intelligent way after lowresolution coding. A novel textureassisted CNN (TACNN) is presented to handle the depth block upsampling. The network is made up of several residual coding units and the features of texture block are extracted to assist the reconstruction of the corresponding depth block. Experimental results show that the proposed method achieves competitive ratedistortion performance compared with the stateoftheart approaches.

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.