mlsanityData to 7 Oct 2026

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

arXiv 1612.01697 ↗First posted 6 Dec 2016cs.CVcited by 224

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Abstract (arXiv)

We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.

Key facts. Cited by 224 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 33; 2022: 37; 2023: 40; 2024: 47; 2025: 35; 2026: 32.

224 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. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  5. 2023TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessmentcited by 220
  6. 2021No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistencycited by 165
  7. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 139
  8. 2023Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Modelscited by 104
  9. 2025Q-Insight: Understanding Image Quality via Visual Reinforcement Learningcited by 93
  10. 2025VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rankcited by 77
  11. 2021CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiabilitycited by 68
  12. 2023VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretrainingcited by 63
38 more
  1. 2023AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondencecited by 61
  2. 2022Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Networkcited by 57
  3. 2023Data-Efficient Image Quality Assessment with Attention-Panel Decodercited by 49
  4. 2021Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessmentcited by 41
  5. 2022NTIRE 2022 Challenge on Perceptual Image Quality Assessmentcited by 41
  6. 2024Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Datasetcited by 38
  7. 2021Continual Learning for Blind Image Quality Assessmentcited by 36
  8. 2024AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessmentcited by 33
  9. 2021Locally Adaptive Structure and Texture Similarity for Image Quality Assessmentcited by 32
  10. 2022Confusing Image Quality Assessment: Towards Better Augmented Reality Experiencecited by 32
  11. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  12. 2024Q-Ground: Image Quality Grounding with Large Multi-modality Modelscited by 30
  13. 2021A Comprehensive Review of Deep Learning-based Single Image Super-resolutioncited by 23
  14. 2023Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessmentcited by 21
  15. 2021Quantifying Visual Image Quality: A Bayesian Viewcited by 20
  16. 2021Discovering Distinctive "Semantics" in Super-Resolution Networkscited by 20
  17. 2021Task-Specific Normalization for Continual Learning of Blind Image Quality Modelscited by 18
  18. 2023Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attackscited by 18
  19. 2023Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessmentcited by 17
  20. 2021No-Reference Image Quality Assessment by Hallucinating Pristine Featurescited by 16
  21. 2025ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Realitycited by 16
  22. 2023Quality-aware Pre-trained Models for Blind Image Quality Assessmentcited by 15
  23. 2024Video Quality Assessment: A Comprehensive Surveycited by 15
  24. 2021Region-Adaptive Deformable Network for Image Quality Assessmentcited by 14
  25. 2021Troubleshooting Blind Image Quality Models in the Wildcited by 14
  26. 2022Content-Variant Reference Image Quality Assessment via Knowledge Distillationcited by 14
  27. 2023PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Imagescited by 14
  28. 2024DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wildcited by 14
  29. 2021RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rankcited by 13
  30. 2024Quality Assessment for AI Generated Images with Instruction Tuningcited by 13
  31. 2024Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wildcited by 13
  32. 2021Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Imagescited by 12
  33. 2021A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metricscited by 12
  34. 2022Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessmentcited by 12
  35. 2024GraFIQs: Face Image Quality Assessment Using Gradient Magnitudescited by 12
  36. 2024UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessmentcited by 12
  37. 2024Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessmentcited by 12
  38. 2023Test Time Adaptation for Blind Image Quality Assessmentcited by 11

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