mlsanityData to 14 Oct 2026

Blind Image Quality Assessment: A Brief Survey

arXiv 2312.16551 ↗First posted 27 Dec 2023cs.CV cs.MMcited by 1

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Abstract (arXiv)

Blind Image Quality Assessment (BIQA) is essential for automatically evaluating the perceptual quality of visual signals without access to the references. In this survey, we provide a comprehensive analysis and discussion of recent developments in the field of BIQA. We have covered various aspects, including hand-crafted BIQAs that focus on distortion-specific and general-purpose methods, as well as deep-learned BIQAs that employ supervised and unsupervised learning techniques. Additionally, we have explored multimodal quality assessment methods that consider interactions between visual and audio modalities, as well as visual and text modalities. Finally, we have offered insights into representative BIQA databases, including both synthetic and authentic distortions. We believe this survey provides valuable understandings into the latest developments and emerging trends for the visual quality community.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 129 references that are in the map. First posted on arXiv 27 Dec 2023.

Builds on

33 of 129 references.

References · 33

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  3. 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,081
  4. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  5. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 811
  6. 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 355
  7. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
  8. 2016Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessmentcited by 225
  9. 2019From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Qualitycited by 223
  10. 2017RankIQA: Learning from Rankings for No-reference Image Quality Assessmentcited by 151
  11. 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144
  12. 2020Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wildcited by 134
21 more
  1. 2016Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challengecited by 116
  2. 2023Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wildcited by 97
  3. 2019dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairscited by 73
  4. 2021Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Trainingcited by 71
  5. 2021SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distancecited by 64
  6. 2020Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessmentcited by 54
  7. 2019Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rankcited by 52
  8. 2016Image Aesthetic Assessment: An Experimental Surveycited by 50
  9. 2018Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learningcited by 49
  10. 2021Continual Learning for Blind Image Quality Assessmentcited by 36
  11. 2022Perceptual Quality Assessment of Omnidirectional Imagescited by 34
  12. 2020Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessmentcited by 24
  13. 2021No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalnesscited by 20
  14. 2019Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Imagecited by 18
  15. 2020No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptationcited by 17
  16. 2019Study of 3D Virtual Reality Picture Qualitycited by 11
  17. 2022Image quality assessment for machine learning tasks using meta-reinforcement learningcited by 10
  18. 2022UIF: An Objective Quality Assessment for Underwater Image Enhancementcited by 10
  19. 2019Harmonized Multimodal Learning with Gaussian Process Latent Variable Modelscited by 6
  20. 2022UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metricscited by 5
  21. 2023Blind Multimodal Quality Assessment of Low-light Imagescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Causally Grounded Taxonomy for Image Degradation Robustness Evaluationcited by 1