mlsanityData to 7 Oct 2026

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

arXiv 1907.02665 ↗First posted 5 Jul 2019eess.IV cs.CV cs.MMcited by 301

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Cited by 21 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortion type and level, where we enjoy large-scale training data. For authentic distortions, we adopt a pre-trained CNN for image classification. The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction. We then fine-tune the entire model on target subject-rated databases using a variant of stochastic gradient descent. Extensive experiments demonstrate that the proposed model achieves superior performance on both synthetic and authentic databases. Furthermore, we verify the generalizability of our method on the Waterloo Exploration Database using the group maximum differentiation competition.

Key facts. Cited by 301 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2019.

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Citing papers per year: 2021: 17; 2022: 36; 2023: 60; 2024: 75; 2025: 67; 2026: 46.

301 papers.

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  1. 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,058
  2. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 974
  3. 2022MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessmentcited by 514
  4. 2023Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levelscited by 489
  5. 2022Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectivescited by 314
  6. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  7. 2023TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessmentcited by 220
  8. 2022FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Samplingcited by 179
  9. 2021No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistencycited by 165
  10. 2022Lossy Image Compression with Conditional Diffusion Modelscited by 165
  11. 2023Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Modelscited by 152
  12. 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
38 more
  1. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 139
  2. 2023AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusioncited by 98
  3. 2021Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perceptioncited by 97
  4. 2023Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wildcited by 97
  5. 2021Image Quality Assessment using Contrastive Learningcited by 95
  6. 2025Q-Insight: Understanding Image Quality via Visual Reinforcement Learningcited by 93
  7. 2023ARNIQA: Learning Distortion Manifold for Image Quality Assessmentcited by 87
  8. 2024Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Comparecited by 66
  9. 2023VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretrainingcited by 63
  10. 2022Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessmentcited by 50
  11. 2023Data-Efficient Image Quality Assessment with Attention-Panel Decodercited by 49
  12. 2023Neural Degradation Representation Learning for All-In-One Image Restorationcited by 46
  13. 2023Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approachcited by 45
  14. 2022NTIRE 2022 Challenge on Perceptual Image Quality Assessmentcited by 41
  15. 2021No-Reference Point Cloud Quality Assessment via Domain Adaptationcited by 37
  16. 2021Continual Learning for Blind Image Quality Assessmentcited by 36
  17. 2024AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessmentcited by 33
  18. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  19. 2023Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluationcited by 27
  20. 2024Large Multi-modality Model Assisted AI-Generated Image Quality Assessmentcited by 24
  21. 2025LMM4LMM: Benchmarking and Evaluating Large-multimodal Image Generation with LMMscited by 24
  22. 2023Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterioncited by 23
  23. 2022Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Modelcited by 22
  24. 2023Exploring the Naturalness of AI-Generated Imagescited by 22
  25. 2023Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Modelscited by 22
  26. 2023Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Imagescited by 21
  27. 2021No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalnesscited by 20
  28. 20242AFC Prompting of Large Multimodal Models for Image Quality Assessmentcited by 20
  29. 2021Task-Specific Normalization for Continual Learning of Blind Image Quality Modelscited by 18
  30. 2023Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attackscited by 18
  31. 2022Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metricscited by 17
  32. 2023Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessmentcited by 17
  33. 2024DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformercited by 17
  34. 2021No-Reference Image Quality Assessment by Hallucinating Pristine Featurescited by 16
  35. 2023Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessmentcited by 16
  36. 2023Quality-aware Pre-trained Models for Blind Image Quality Assessmentcited by 15
  37. 2024Video Quality Assessment: A Comprehensive Surveycited by 15
  38. 2021Troubleshooting Blind Image Quality Models in the Wildcited by 14

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