Assessing Image Quality Issues for Real-World Problems
Open accessAbstract
We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering. First, we identify for 39,181 images taken by people who are blind whether each is sufficient quality to recognize the content as well as what quality flaws are observed from six options. These labels serve as a critical foundation for us to make the following contributions: (1) a new problem and algorithms for deciding whether an image is insufficient quality to recognize the content and so not captionable, (2) a new problem and algorithms for deciding which of six quality flaws an image contains, (3) a new problem and algorithms for deciding whether a visual question is unanswerable due to unrecognizable content versus the content of interest being missing from the field of view, and (4) a novel application of more efficiently creating a large-scale image captioning dataset by automatically deciding whether an image is insufficient quality and so should not be captioned. We publicly-share our datasets and code to facilitate future extensions of this work: https://vizwiz.org.
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Co-occurring quality issues are common in BLV people's photos.
“and are common in BLV people's photos [30]”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [30]
In “Relationship Between Quality Flaws in Images” (page unavailable), the paper says almost all quality flaws tend to occur with one another in its dataset of photos taken by people who are blind. It does not establish how often multiple flaws appear in photos overall, or extend the finding specifically to people with low vision, whom “BLV” also includes. (LLM, conf=0.81)
Experimental — accuracy not yet measuredThe prevalence of low-quality images has made image quality assessment a stand-alone task in developing image captioning tools for BLV individuals.
“The prevalence of low-quality images has made image quality assessment a stand-alone task in developing image captioning tools for BLV individuals [30].”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [30]
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SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking
Fang, Z., Ignatov, A., Zamfir, E., & Timofte, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01877 · Cited by 5 · see citing works
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“Building on our survey findings, we then develop a structured, annotated dataset of 1,859 naturalistic product images (based on the VizWiz dataset [30, 56]) and use it to evaluate how robust four top-performing VLMs—GPT-4.”
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Explaining CLIP's Performance Disparities on Data from Blind/Low Vision Users
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LLM-TTA: Leveraging Large Language Models for Test-Time Adaptation in Image Quality Assessment
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- Authors
- Chiu, T.-Y., Zhao, Y., & Gurari, D.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 3643-3653
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3013253492
- Cited by
- 63
- References
- 62
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 11 · 2023: 12 · 2022: 14 · 2021: 5 · 2020: 5
- FWCI
- 2.78
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1755593)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- image quality assessment, blind users, image captioning, visual question answering, unanswerable questions, low-quality images, content recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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