# A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video

arXiv: https://arxiv.org/abs/2311.08746 · HTML: https://mlsanity.com/paper/2311.08746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023)
- [MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video](https://mlsanity.com/paper/1902.09707/) (2019)
- [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)
- [DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder](https://mlsanity.com/paper/2206.00386/) (2022)
- [Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs](https://mlsanity.com/paper/2305.12170/) (2023)
- [An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding](https://mlsanity.com/paper/2012.15003/) (2020)
- [Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation](https://mlsanity.com/paper/2308.08339/) (2023)

## Abstract (arXiv)

Video post-processing methods can improve the quality of compressed videos at the decoder side. Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of compressed videos. However, in most cases, the quantization parameters of the decoded video are unknown. This makes existing methods have their limitations in improving video quality. To tackle this problem, this work proposes a diffusion model based post-processing method for compressed videos. The proposed method first estimates the feature vectors of the compressed video and then uses the estimated feature vectors as the prior information for the quality enhancement model to adaptively enhance the quality of compressed video with different quantization parameters. Experimental results show that the quality enhancement results of our proposed method on mixed datasets are superior to existing methods.