论文标题
MFRNET：用于后处理和环内过滤的新的CNN体系结构
MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering
论文作者
论文摘要
在本文中，我们提出了一种新型的卷积神经网络（CNN）体系结构MFRNET，用于后处理（PP）和环内滤波（ILF）在视频压缩的上下文中。该网络由四个多级特征审查剩余密度块（MFRB）组成，这些块（MFRB）是使用级联结构连接的。每个MFRB都使用密集的连接和多层残留学习结构从多个卷积层提取特征。为了进一步改善这些块之间的信息流，每个块还重新恢复了上一个MFRB的高维特征。该网络已集成到HEVC（HM 16.20）和VVC（VTM 7.0）的PP和ILF编码模块中，并使用随机访问配置在JVET公共测试条件下进行了充分评估。实验结果表明，基于Bjontegaard Delta测量结果，使用PSNR和VMAF进行质量评估，基于Bjontegaard Delta测量结果，基于Bjontegaard Delta测量结果，锚定解码器（HEVC HM和VVC VTM）以及其他现有的基于CNN的PP/ILF方法都显示出显着且一致的编码收益。当将MFRNET集成到HM 16.20中时，ILF的增长率高达16.0％（BD速率VMAF），PP的PP最高为21.0％（BD率VMAF）。 ILF的VTM 7.0的各自收益最高可达5.1％，PP最高为7.1％。
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.
