mlsanityData to 7 Oct 2026

PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration

arXiv 2007.12142 ↗First posted 23 Jul 2020eess.IV cs.CVcited by 177

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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method.

Key facts. Cited by 177 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.

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Citing papers per year: 2021: 15; 2022: 30; 2023: 23; 2024: 39; 2025: 37; 2026: 33.

177 papers.

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  1. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 974
  2. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 524
  3. 2022MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessmentcited by 514
  4. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
  5. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  6. 2023TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessmentcited by 220
  7. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 139
  8. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  9. 2023Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Modelscited by 104
  10. 2023AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusioncited by 98
  11. 2025Q-Insight: Understanding Image Quality via Visual Reinforcement Learningcited by 93
  12. 2024Towards Open-ended Visual Quality Comparisoncited by 85
38 more
  1. 2024AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillationcited by 66
  2. 2022Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Networkcited by 57
  3. 2023Masked Image Training for Generalizable Deep Image Denoisingcited by 57
  4. 2021Perceptual Image Quality Assessment with Transformerscited by 54
  5. 2025DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolutioncited by 50
  6. 2023Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streakscited by 45
  7. 2022Shift-tolerant Perceptual Similarity Metriccited by 44
  8. 2021Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessmentcited by 41
  9. 2021Reflash Dropout in Image Super-Resolutioncited by 41
  10. 2022NTIRE 2022 Challenge on Perceptual Image Quality Assessmentcited by 41
  11. 2024RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Modelscited by 41
  12. 2024Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Datasetcited by 38
  13. 2022PyTorch Image Quality: Metrics for Image Quality Assessmentcited by 34
  14. 2025Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumptioncited by 34
  15. 2025Harnessing Diffusion-Yielded Score Priors for Image Restorationcited by 34
  16. 2024Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessmentcited by 31
  17. 2025Lossy Compression with Pretrained Diffusion Modelscited by 23
  18. 2023Exploring the Naturalness of AI-Generated Imagescited by 22
  19. 2023Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolutioncited by 21
  20. 2024NTIRE 2024 Quality Assessment of AI-Generated Content Challengecited by 20
  21. 2021A Systematic Survey of Deep Learning-based Single-Image Super-Resolutioncited by 19
  22. 2024Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learningcited by 19
  23. 2021NTIRE 2021 Challenge on Perceptual Image Quality Assessmentcited by 18
  24. 2022Evaluating the Generalization Ability of Super-Resolution Networkscited by 18
  25. 2022Efficient Image Super-Resolution using Vast-Receptive-Field Attentioncited by 16
  26. 2021Toward Interactive Modulation for Photo-Realistic Image Restorationcited by 15
  27. 2024Video Quality Assessment: A Comprehensive Surveycited by 15
  28. 2025Dual Prompting Image Restoration with Diffusion Transformerscited by 15
  29. 2021Region-Adaptive Deformable Network for Image Quality Assessmentcited by 14
  30. 2022SwinIQA: Learned Swin Distance for Compressed Image Quality Assessmentcited by 14
  31. 2023Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Methodcited by 14
  32. 2023Networks are Slacking Off: Understanding Generalization Problem in Image Derainingcited by 14
  33. 2024DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wildcited by 14
  34. 2025UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Spacecited by 14
  35. 2025OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolutioncited by 14
  36. 2021Contrastive Feature Loss for Image Predictioncited by 12
  37. 2022Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessmentcited by 12
  38. 2024Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Modelcited by 12

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