# CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement

arXiv: https://arxiv.org/abs/2403.10362 · HTML: https://mlsanity.com/paper/2403.10362/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 26 of 26)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Residual Non-local Attention Networks for Image Restoration](https://mlsanity.com/paper/1903.10082/) (2019)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video](https://mlsanity.com/paper/1902.09707/) (2019)
- [BVI-DVC: A Training Database for Deep Video Compression](https://mlsanity.com/paper/2003.13552/) (2020)
- [Multi-Frame Quality Enhancement for Compressed Video](https://mlsanity.com/paper/1803.04680/) (2018)
- [Enhancing Quality for HEVC Compressed Videos](https://mlsanity.com/paper/1709.06734/) (2017)
- [A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding](https://mlsanity.com/paper/1608.06690/) (2016)
- [Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment](https://mlsanity.com/paper/2304.06440/) (2023)
- [NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results](https://mlsanity.com/paper/2204.09314/) (2022)
- [Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment](https://mlsanity.com/paper/2308.00729/) (2023)
- [DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection](https://mlsanity.com/paper/1412.5661/) (2014)
- [Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment](https://mlsanity.com/paper/2307.16813/) (2023)
- [Quality-aware Pre-trained Models for Blind Image Quality Assessment](https://mlsanity.com/paper/2303.00521/) (2023)
- [Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction](https://mlsanity.com/paper/2108.02110/) (2021)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study](https://mlsanity.com/paper/2104.10782/) (2021)
- [Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network](https://mlsanity.com/paper/1805.03894/) (2018)
- [Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC](https://mlsanity.com/paper/1912.11604/) (2019)
- [A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution](https://mlsanity.com/paper/2210.08229/) (2022)
- [Blind Image Super-resolution with Rich Texture-Aware Codebooks](https://mlsanity.com/paper/2310.17188/) (2023)
- [Transcoded Video Restoration by Temporal Spatial Auxiliary Network](https://mlsanity.com/paper/2112.07948/) (2021)
- [Tencent Video Dataset (TVD): A Video Dataset for Learning-based Visual Data Compression and Analysis](https://mlsanity.com/paper/2105.05961/) (2021)

## Built on by (most cited first, 17 of 17)

- [Plug-and-Play Tri-Branch Invertible Block for Image Rescaling](https://mlsanity.com/paper/2412.13508/) (2024)
- [Plug-and-Play Versatile Compressed Video Enhancement](https://mlsanity.com/paper/2504.15380/) (2025)
- [A New Dataset and Framework for Real-World Blurred Images Super-Resolution](https://mlsanity.com/paper/2407.14880/) (2024)
- [OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal](https://mlsanity.com/paper/2408.11480/) (2024)
- [CodingHomo: Bootstrapping Deep Homography With Video Coding](https://mlsanity.com/paper/2504.12165/) (2025)
- [Coding-Prior Guided Diffusion Network for Video Deblurring](https://mlsanity.com/paper/2504.12222/) (2025)
- [Compressed Video Super-Resolution based on Hierarchical Encoding](https://mlsanity.com/paper/2506.14381/) (2025)
- [Trajectory-aware Shifted State Space Models for Online Video Super-Resolution](https://mlsanity.com/paper/2508.10453/) (2025)
- [Compressed Video Quality Enhancement: Classifying and Benchmarking over Standards](https://mlsanity.com/paper/2509.10407/) (2025)
- [CompSplat: Compression-aware 3D Gaussian Splatting for Real-world Video](https://mlsanity.com/paper/2602.09816/) (2026)
- [Auditing Generalization in AI-Generated Video Detection: A Six-Control Protocol and the VidAudit Toolkit](https://mlsanity.com/paper/2606.31004/) (2026)
- [Compression-Realized Deep Structural Network for Video Quality Enhancement](https://mlsanity.com/paper/2405.06342/) (2024)
- [HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement](https://mlsanity.com/paper/2504.02373/) (2025)
- [DiffCVE: Diffusion-based Compressed Video Enhancement](https://mlsanity.com/paper/2607.07195/) (2026)
- [LPM: Industrial-Scale Generative Video Restoration](https://mlsanity.com/paper/2607.13460/) (2026)
- [Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis](https://mlsanity.com/paper/2607.16319/) (2026)
- [MDFI: A Multi-Domain Features Integration for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2608.21495/) (2026)

## Abstract (arXiv)

Recently, numerous approaches have achieved notable success in compressed video quality enhancement (VQE). However, these methods usually ignore the utilization of valuable coding priors inherently embedded in compressed videos, such as motion vectors and residual frames, which carry abundant temporal and spatial information. To remedy this problem, we propose the Coding Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial information from coding priors. The CPGA mainly consists of an inter-frame temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA) module. Specifically, the ITA module aggregates temporal information from consecutive frames and coding priors, while the MNA module globally captures spatial information guided by residual frames. In addition, to facilitate research in VQE task, we newly construct the Video Coding Priors (VCP) dataset, comprising 300 videos with various coding priors extracted from corresponding bitstreams. It remedies the shortage of previous datasets on the lack of coding information. Experimental results demonstrate the superiority of our method compared to existing state-of-the-art methods. The code and dataset will be released at https://github.com/VQE-CPGA/CPGA.git .