Deep Learning-Based Chroma Prediction for Intra Versatile Video Coding

Color images always exhibit a high correlation between luma and chroma components. Cross component linear model (CCLM) has been introduced to exploit such correlation for removing redundancy in the on-going video coding standard, i.e., versatile video coding (VVC). To further improve the coding performance, this paper presents a deep learning based intra chroma prediction method, termed as convolutional neural network based chroma prediction (CNNCP). More specifically, the process of chroma prediction is formulated to produce the colorful version from available information input. CNNCP includes two sub-networks for luma down-sampling and chroma prediction, which are jointly optimized to fully exploit spatial and cross component information. In addition, the outputs of CCLM are adopted as chroma initialization for performance enhancement, and the coding distortion level characterized by quantization parameter is fed into the network to release the negative affect from compression artifacts. To further improve the coding performance, the competition is performed between the conventional chroma prediction and CNNCP in terms of rate-distortion cost with a binary flag signalled. The learned CNNCP is incorporated into both video encoder and decoder. Extensive experimental results demonstrate that the proposed scheme can achieve 4.283%, 3.343%, and 4.634% bit rate savings for luma and two chroma components, compared with the VVC test model version 4.0 (VTM 4.0).