# Enhancing Quality for HEVC Compressed Videos

arXiv: https://arxiv.org/abs/1709.06734 · HTML: https://mlsanity.com/paper/1709.06734/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 28 of 28)

- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results](https://mlsanity.com/paper/2204.09314/) (2022)
- [AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results](https://mlsanity.com/paper/2208.11184/) (2022)
- [CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.10362/) (2024)
- [Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction](https://mlsanity.com/paper/2108.02110/) (2021)
- [NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results](https://mlsanity.com/paper/2505.03007/) (2025)
- [Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration](https://mlsanity.com/paper/2205.10195/) (2022)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study](https://mlsanity.com/paper/2104.10782/) (2021)
- [End-to-end Transformer for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2210.13827/) (2022)
- [Transcoded Video Restoration by Temporal Spatial Auxiliary Network](https://mlsanity.com/paper/2112.07948/) (2021)
- [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)
- [Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information](https://mlsanity.com/paper/2105.14962/) (2021)
- [Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement](https://mlsanity.com/paper/2202.00011/) (2022)
- [Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model](https://mlsanity.com/paper/2504.19935/) (2025)
- [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)
- [Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection](https://mlsanity.com/paper/2202.09802/) (2022)
- [Leveraging Video Coding Knowledge for Deep Video Enhancement](https://mlsanity.com/paper/2302.13594/) (2023)
- [Lightweight Hybrid Video Compression Framework Using Reference-Guided Restoration Network](https://mlsanity.com/paper/2303.11592/) (2023)
- [A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video](https://mlsanity.com/paper/2311.08746/) (2023)
- [Compression-Realized Deep Structural Network for Video Quality Enhancement](https://mlsanity.com/paper/2405.06342/) (2024)
- [FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution](https://mlsanity.com/paper/2506.11545/) (2025)
- [Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference](https://mlsanity.com/paper/2511.16137/) (2025)
- [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)
- [Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement](https://mlsanity.com/paper/2609.39894/) (2026)

## Abstract (arXiv)

The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to generate video streams over the Internet. However, HEVC compressed videos may incur severe quality degradation, particularly at low bit-rates. Thus, it is necessary to enhance the visual quality of HEVC videos at the decoder side. To this end, this paper proposes a Quality Enhancement Convolutional Neural Network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC. In particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the distortion of HEVC I and P frames, respectively. The proposed method differs from the existing CNN-based quality enhancement approaches, which only handle intra-coding distortion and are thus not suitable for P frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos. To apply our QE-CNN method in time-constrained scenarios, we further propose a Time-constrained Quality Enhancement Optimization (TQEO) scheme. Our TQEO scheme controls the computational time of QE-CNN to meet a target, meanwhile maximizing the quality enhancement. Next, the experimental results demonstrate the effectiveness of our TQEO scheme from the aspects of time control accuracy and quality enhancement under different time constraints. Finally, we design a prototype to implement our TQEO scheme in a real-time scenario.