mlsanityData to 14 Oct 2026

Blind Predicting Similar Quality Map for Image Quality Assessment

arXiv 1805.08493 ↗First posted 22 May 2018cs.CVcited by 21

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Abstract (arXiv)

A key problem in blind image quality assessment (BIQA) is how to effectively model the properties of human visual system in a data-driven manner. In this paper, we propose a simple and efficient BIQA model based on a novel framework which consists of a fully convolutional neural network (FCNN) and a pooling network to solve this problem. In principle, FCNN is capable of predicting a pixel-by-pixel similar quality map only from a distorted image by using the intermediate similarity maps derived from conventional full-reference image quality assessment methods. The predicted pixel-by-pixel quality maps have good consistency with the distortion correlations between the reference and distorted images. Finally, a deep pooling network regresses the quality map into a score. Experiments have demonstrated that our predictions outperform many state-of-the-art BIQA methods.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 May 2018.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 3; 2024: 2; 2025: 4; 2026: 1.

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  1. 2021No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistencycited by 166
  2. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 141
  3. 2023Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Modelscited by 105
  4. 2025Q-Insight: Understanding Image Quality via Visual Reinforcement Learningcited by 98
  5. 2021Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessmentcited by 41
  6. 2024Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Datasetcited by 38
  7. 2021Quantifying Visual Image Quality: A Bayesian Viewcited by 20
  8. 2024DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wildcited by 14
  9. 2021Assessing a Single Image in Reference-Guided Image Synthesiscited by 13
  10. 2022Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessmentcited by 9
  11. 2022KonX: Cross-Resolution Image Quality Assessmentcited by 9
  12. 2022REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effectcited by 6
9 more
  1. 2025UARE: A Unified Vision-Language Model for Image Quality Assessment, Restoration, and Enhancementcited by 5
  2. 2021Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Videocited by 4
  3. 2026How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practicescited by 2
  4. 2021Quality Map Fusion for Adversarial Learningcited by 1
  5. 2021Learning Transformer Features for Image Quality Assessmentcited by 1
  6. 2023SAM-IQA: Can Segment Anything Boost Image Quality Assessment?cited by 1
  7. 2022Residual-Guided Non-Intrusive Speech Quality Assessmentcited by 0
  8. 2023Mask Reference Image Quality Assessmentcited by 0
  9. 2025Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessmentcited by 0