mlsanityData to 14 Oct 2026

A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video

arXiv 2311.08746 ↗First posted 15 Nov 2023eess.IV cs.CVcited by 0

On the map: Diffusion models → Inverse problems and image restoration

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.

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.

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11 of 25 references.

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Same direction, newest first

  1. 2023Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs20 May
  2. 2023DiffIR: Efficient Diffusion Model for Image Restoration16 Mar

Other references · 9

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  3. 2019MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Videocited by 50
  4. 2018Multi-Frame Quality Enhancement for Compressed Videocited by 48
  5. 2017Enhancing Quality for HEVC Compressed Videoscited by 28
  6. 2016A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Codingcited by 27
  7. 2022DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decodercited by 27
  8. 2020An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Codingcited by 4
  9. 2023Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentationcited by 4

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  1. 2026DiffCVE: Diffusion-based Compressed Video Enhancement
  2. 2024Noise Calibration: Plug-and-play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models
  3. 2024QVD: Post-training Quantization for Video Diffusion Models
  4. 2025Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution
  5. 2025Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse
  6. 2024Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression
  7. 2026DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer
  8. 2026How Accurate are Video Quality Models for Diffusion-Based Video Super-Resolution?