mlsanityData to 7 Oct 2026

Quantization Guided JPEG Artifact Correction

arXiv 2004.09320 ↗First posted 17 Apr 2020eess.IV cs.CV cs.LGcited by 57

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Abstract (arXiv)

The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios. However, to achieve such high compression, information is lost. For aggressive quantization settings, this leads to a noticeable reduction in image quality. Artifact correction has been studied in the context of deep neural networks for some time, but the current state-of-the-art methods require a different model to be trained for each quality setting, greatly limiting their practical application. We solve this problem by creating a novel architecture which is parameterized by the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2020.

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Citing papers per year: 2021: 7; 2022: 15; 2023: 12; 2024: 11; 2025: 10; 2026: 2.

57 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  2. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  3. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  4. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  5. 2021Towards Flexible Blind JPEG Artifacts Removalcited by 76
  6. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 72
  7. 2022JPEG Artifact Correction using Denoising Diffusion Restoration Modelscited by 45
  8. 2023A Residual Diffusion Model for High Perceptual Quality Codec Augmentationcited by 33
  9. 2022Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domaincited by 28
  10. 2025Visual-Instructed Degradation Diffusion for All-in-One Image Restorationcited by 26
  11. 2022Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolutioncited by 25
  12. 2022Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolutioncited by 22
38 more
  1. 2023A Unified Conditional Framework for Diffusion-based Image Restorationcited by 22
  2. 2022AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Resultscited by 20
  3. 2024PromptCIR: Blind Compressed Image Restoration with Prompt Learningcited by 20
  4. 2022DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domaincited by 18
  5. 2022RGB no more: Minimally-decoded JPEG Vision Transformerscited by 18
  6. 2021Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeletoncited by 16
  7. 2022DriftRec: Adapting diffusion models to blind JPEG restorationcited by 15
  8. 2024Sharing Key Semantics in Transformer Makes Efficient Image Restorationcited by 14
  9. 2025Compression-Aware One-Step Diffusion Model for JPEG Artifact Removalcited by 13
  10. 2023Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tablescited by 11
  11. 2025Reversing Flow for Image Restorationcited by 10
  12. 2021Functional Neural Networks for Parametric Image Restoration Problemscited by 9
  13. 2022High-Perceptual Quality JPEG Decoding via Posterior Samplingcited by 8
  14. 2024JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficientscited by 7
  15. 2023Real-time 6K Image Rescaling with Rate-distortion Optimizationcited by 6
  16. 2023Restore Anything Pipeline: Segment Anything Meets Image Restorationcited by 6
  17. 2024Sensitivity Decouple Learning for Image Compression Artifacts Reductioncited by 5
  18. 2021Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutioncited by 4
  19. 2022Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domaincited by 4
  20. 2022DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentationcited by 3
  21. 2023Dual-layer Image Compression via Adaptive Downsampling and Spatially Varying Upconversioncited by 3
  22. 2023JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformercited by 3
  23. 2024Key-Graph Transformer for Image Restorationcited by 3
  24. 2024OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removalcited by 3
  25. 2022Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancementcited by 2
  26. 2022Compressing Sign Information in DCT-based Image Coding via Deep Sign Retrievalcited by 2
  27. 2023Picking Up Quantization Steps for Compressed Image Classificationcited by 2
  28. 2024Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domaincited by 2
  29. 2022Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolutioncited by 1
  30. 2023Learned Lossless Compression for JPEG via Frequency-Domain Predictioncited by 1
  31. 2024CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancementcited by 1
  32. 2025Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptationcited by 1
  33. 2025Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlationscited by 1
  34. 2026Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passescited by 1
  35. 2021TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compressioncited by 0
  36. 2023CNNs for JPEGs: A Study in Computational Costcited by 0
  37. 2024Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancementcited by 0
  38. 2024Exploring Distortion Prior with Latent Diffusion Models for Remote Sensing Image Compressioncited by 0

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