# Compressed Video Quality Enhancement: Classifying and Benchmarking over Standards

arXiv: https://arxiv.org/abs/2509.10407 · HTML: https://mlsanity.com/paper/2509.10407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 73 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [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)
- [Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video](https://mlsanity.com/paper/2008.00499/) (2020)
- [CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.10362/) (2024)
- [End-to-end Transformer for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2210.13827/) (2022)
- [DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video](https://mlsanity.com/paper/2201.08944/) (2022)
- [Compressed Video Quality Enhancement with Temporal Group Alignment and Fusion](https://mlsanity.com/paper/2406.09693/) (2024)
- [Plug-and-Play Versatile Compressed Video Enhancement](https://mlsanity.com/paper/2504.15380/) (2025)
- [MGANet: A Robust Model for Quality Enhancement of Compressed Video](https://mlsanity.com/paper/1811.09150/) (2018)
- [A CNN-based Prediction-Aware Quality Enhancement Framework for VVC](https://mlsanity.com/paper/2105.05658/) (2021)
- [Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement](https://mlsanity.com/paper/2202.00011/) (2022)
- [NU-Class Net: A Novel Approach for Video Quality Enhancement](https://mlsanity.com/paper/2401.01163/) (2024)
- [Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model](https://mlsanity.com/paper/2504.19935/) (2025)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [Online Video Quality Enhancement with Spatial-Temporal Look-up Tables](https://mlsanity.com/paper/2311.13616/) (2023)
- [PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network](https://mlsanity.com/paper/2206.07893/) (2022)
- [Valid Information Guidance Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2303.00520/) (2023)
- [Joint Reference Frame Synthesis and Post Filter Enhancement for Versatile Video Coding](https://mlsanity.com/paper/2404.18058/) (2024)

## Built on by (most cited first, 1 of 1)

- [MDFI: A Multi-Domain Features Integration for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2608.21495/) (2026)

## Abstract (arXiv)

Compressed video quality enhancement (CVQE) is crucial for improving user experience with lossy video codecs like H.264/AVC, H.265/HEVC, and H.266/VVC. While deep learning based CVQE has driven significant progress, existing surveys still suffer from limitations: lack of systematic classification linking methods to specific standards and artifacts, insufficient comparative analysis of architectural paradigms across coding types, and underdeveloped benchmarking practices. To address these gaps, this paper presents three key contributions. First, it introduces a novel taxonomy classifying CVQE methods across architectural paradigms, coding standards, and compressed-domain feature utilization. Second, it proposes a unified benchmarking framework integrating modern compression protocols and standard test sequences for fair multi-criteria evaluation. Third, it provides a systematic analysis of the critical trade-offs between reconstruction performance and computational complexity observed in state-of-the-art methods and highlighting promising directions for future research. This comprehensive review aims to establish a foundation for consistent assessment and informed model selection in CVQE research and deployment.