mlsanityData to 7 Oct 2026

Image Quality Assessment in the Modern Age

arXiv 2110.09699 ↗First posted 19 Oct 2021cs.CV eess.IVcited by 2

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Abstract (arXiv)

This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA). From an actionable perspective, we will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. We will then present in detail the design principles of objective quality assessment models, supplemented by an in-depth analysis of their advantages and disadvantages. Both hand-engineered and (deep) learning-based methods will be covered. Moreover, the limitations with the conventional model comparison methodology for objective quality models will be pointed out, and novel comparison methodologies such as those based on the theory of "analysis by synthesis" will be introduced. We will last discuss the real-world multimedia applications of IQA, and give a list of open challenging problems, in the hope of encouraging more and more talented researchers and engineers devoting to this exciting and rewarding research field.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

6 of 28 references.

References · 6

  1. 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 351
  2. 2015Massive Online Crowdsourced Study of Subjective and Objective Picture Qualitycited by 208
  3. 2020Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wildcited by 134
  4. 2021Continual Learning for Blind Image Quality Assessmentcited by 36
  5. 2017Eigen-Distortions of Hierarchical Representationscited by 25
  6. 2021Troubleshooting Blind Image Quality Models in the Wildcited by 14

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.