Distortion-based Neural Network for Compression Artifacts Reduction in VVC
Jian Qing Qian, Hongkui Wang, Li Chen Yu · 2021 International Conference on Visual Communications and Image Processing (VCIP) · 2021
To reduce compression artifacts in video coding, prior knowledge from the codec is often utilized by deep learning based enhancement methods. However, most existing algorithms only take limited kinds of prior knowledge into consideration and directly feed it into neural networks, resulting in finite information obtained by the enhancement module. In this paper, a distortion-based neural network is proposed to better take advantage of features from the codec and further facilitate the quality of decoded videos. Firstly, a variety of prior knowledge is unitedly exploited to estimate the compression distortion. Secondly, an estimation module is also designed for the information obtained from the codec to improve the precision of estimation. With the accurately estimated distortion level, the enhancement module in the proposed method could reduce the corresponding artifacts by flexibly controlling the filtering strength. Moreover, the proposed method is integrated into VTM-7.1, achieving on average 5.92% BD-rate saving for Y component under All Intra configuration.