mlsanityData to 7 Oct 2026

Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild

arXiv 2005.13983 ↗First posted 28 May 2020cs.CV cs.LG cs.MMcited by 134

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Abstract (arXiv)

Performance of blind image quality assessment (BIQA) models has been significantly boosted by end-to-end optimization of feature engineering and quality regression. Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa). To confront the cross-distortion-scenario challenge, we develop a textitunified BIQA model and an approach of training it for both synthetic and realistic distortions. We first sample pairs of images from individual IQA databases, and compute a probability that the first image of each pair is of higher quality. We then employ the fidelity loss to optimize a deep neural network for BIQA over a large number of such image pairs. We also explicitly enforce a hinge constraint to regularize uncertainty estimation during optimization. Extensive experiments on six IQA databases show the promise of the learned method in blindly assessing image quality in the laboratory and wild. In addition, we demonstrate the universality of the proposed training strategy by using it to improve existing BIQA models.

Key facts. Cited by 134 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2020.

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Citing papers per year: 2021: 16; 2022: 22; 2023: 16; 2024: 29; 2025: 31; 2026: 20.

134 papers.

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  1. 2022Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectivescited by 314
  2. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  3. 2021No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistencycited by 165
  4. 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
  5. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 139
  6. 2022A Deep Learning based No-reference Quality Assessment Model for UGC Videoscited by 128
  7. 2021Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perceptioncited by 97
  8. 2024Towards Open-ended Visual Quality Comparisoncited by 85
  9. 2025VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rankcited by 77
  10. 2024Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Comparecited by 66
  11. 2021Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancementcited by 60
  12. 2022Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessmentcited by 50
38 more
  1. 2024A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessmentcited by 43
  2. 2023Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Modelscited by 38
  3. 2021No-Reference Point Cloud Quality Assessment via Domain Adaptationcited by 37
  4. 2021Continual Learning for Blind Image Quality Assessmentcited by 36
  5. 2025Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumptioncited by 34
  6. 2024AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessmentcited by 33
  7. 2021Semantically Contrastive Learning for Low-light Image Enhancementcited by 31
  8. 2022Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loopcited by 30
  9. 2024LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Modelscited by 28
  10. 2022Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessmentcited by 25
  11. 2023Exploring the Naturalness of AI-Generated Imagescited by 22
  12. 2022CONVIQT: Contrastive Video Quality Estimatorcited by 19
  13. 2021Task-Specific Normalization for Continual Learning of Blind Image Quality Modelscited by 18
  14. 2022Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyondcited by 17
  15. 2024PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wildcited by 17
  16. 2024DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformercited by 17
  17. 2021No-Reference Image Quality Assessment by Hallucinating Pristine Featurescited by 16
  18. 2023Quality-aware Pre-trained Models for Blind Image Quality Assessmentcited by 15
  19. 2021Troubleshooting Blind Image Quality Models in the Wildcited by 14
  20. 2022Deep Neural Network for Blind Visual Quality Assessment of 4K Contentcited by 14
  21. 2024PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Promptscited by 14
  22. 2021Assessing a Single Image in Reference-Guided Image Synthesiscited by 13
  23. 2023An Image Quality Assessment Dataset for Portraitscited by 13
  24. 2024Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Modelcited by 13
  25. 2025AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Imagescited by 13
  26. 2021A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metricscited by 12
  27. 2022Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrievalcited by 12
  28. 2024UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessmentcited by 12
  29. 2024Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Triggercited by 12
  30. 2022MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusioncited by 10
  31. 2024Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Methodcited by 10
  32. 2025Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Rankingcited by 10
  33. 2021SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Derainingcited by 9
  34. 2022Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessmentcited by 9
  35. 2024Assessing UHD Image Quality from Aesthetics, Distortions, and Saliencycited by 9
  36. 2024Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metriccited by 9
  37. 2022Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Qualitycited by 8
  38. 2023Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivitycited by 8

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