  In this paper, we propose a novel convolutional neural network (CNN)
architecture, MFRNet, for post-processing (PP) and in-loop filtering (ILF) in
the context of video compression. This network consists of four Multi-level
Feature review Residual dense Blocks (MFRBs), which are connected using a
cascading structure. Each MFRB extracts features from multiple convolutional
layers using dense connections and a multi-level residual learning structure.
In order to further improve information flow between these blocks, each of them
also reuses high dimensional features from the previous MFRB. This network has
been integrated into PP and ILF coding modules for both HEVC (HM 16.20) and VVC
(VTM 7.0), and fully evaluated under the JVET Common Test Conditions using the
Random Access configuration. The experimental results show significant and
consistent coding gains over both anchor codecs (HEVC HM and VVC VTM) and also
over other existing CNN-based PP/ILF approaches based on Bjontegaard Delta
measurements using both PSNR and VMAF for quality assessment. When MFRNet is
integrated into HM 16.20, gains up to 16.0% (BD-rate VMAF) are demonstrated for
ILF, and up to 21.0% (BD-rate VMAF) for PP. The respective gains for VTM 7.0
are up to 5.1% for ILF and up to 7.1% for PP.
