# MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering

arXiv: https://arxiv.org/abs/2007.07099 · HTML: https://mlsanity.com/paper/2007.07099/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [NVRC: Neural Video Representation Compression](https://mlsanity.com/paper/2409.07414/) (2024)
- [PNVC: Towards Practical INR-based Video Compression](https://mlsanity.com/paper/2409.00953/) (2024)
- [HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution](https://mlsanity.com/paper/2412.03748/) (2024)
- [Immersive Video Compression using Implicit Neural Representations](https://mlsanity.com/paper/2402.01596/) (2024)
- [Compressing Deep Image Super-resolution Models](https://mlsanity.com/paper/2401.00523/) (2023)
- [CARNet:Compression Artifact Reduction for Point Cloud Attribute](https://mlsanity.com/paper/2209.08276/) (2022)
- [RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content](https://mlsanity.com/paper/2411.13362/) (2024)
- [Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation](https://mlsanity.com/paper/2312.02605/) (2023)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [Standard compliant video coding using low complexity, switchable neural wrappers](https://mlsanity.com/paper/2407.07395/) (2024)
- [Joint Reference Frame Synthesis and Post Filter Enhancement for Versatile Video Coding](https://mlsanity.com/paper/2404.18058/) (2024)
- [DaBiT: Depth and Blur informed Transformer for Video Focal Deblurring](https://mlsanity.com/paper/2407.01230/) (2024)
- [Compressed Video Super-Resolution based on Hierarchical Encoding](https://mlsanity.com/paper/2506.14381/) (2025)
- [Prediction-Aware Quality Enhancement of VVC Using CNN](https://mlsanity.com/paper/2112.04225/) (2021)
- [Complexity Reduction of Learned In-Loop Filtering in Video Coding](https://mlsanity.com/paper/2203.08650/) (2022)
- [Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis](https://mlsanity.com/paper/2607.16319/) (2026)

## Abstract (arXiv)

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.