Meta-ILF: In-Loop Filter with Customized Weights For VVC Intra Coding

Hengyu Man, Xingtao Wang, Riyu Lu, Xiaopeng Fan

Abstract: In-Loop filter (ILF) is an essential module in video coding for suppressing compression artifacts and thus improving the quality of reconstructed images. As the state-of-the-art video coding standard, Versatile Video Coding (H.266/VVC) employs three in-loop filters, including deblocking filter, sample adaptive offset, and adaptive loop filter. Recently, many neural network-based in-loop filters have been proposed and shown great success in image restoration. In this paper, we propose a meta-learning-based method called Meta-ILF, which performs filtering with customized weights to enhance the quality of VVC intra-coded images. Meta-ILF consists of a meta-network and a filter network. For each reconstructed image block, the meta-network generates the customized weights first. Then, the filter network uses the customized weights to infer the enhanced reconstruction. By dynamically customizing the network weights for each reconstructed block, meta-ILF can better cope with the diverse compression artifacts. To test the performance, Meta-ILF is integrated into VVC reference software VTM-11.0. The experimental results demonstrate that Meta-ILF can reach an average of 6.77% Bjøntegaard Delta rate (BD-rate) improvement over VVC with all intra configuration.