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A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection
Abstract
In this paper, a no-reference objective sharpness metric based on a cumulative probability of blur detection is proposed. The metric is evaluated by taking into account the human visual system (HVS) response to blur distortions. The perceptual significance of the metric is validated through subjective experiments. It is shown that the proposed metric results in a very good correlation with subjective scores especially for images with varying foreground and background perceived blur qualities. This is accomplished with a significantly lower computational complexity as compared to existing methods that take into account the visual attention information.
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“Cumulative Probability Blur Detection (CPBD) [119], a non-reference measure, is also widely used to evaluate the loss of sharpness during video generation.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [119]
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Metadata
- Authors
- Narvekar, N. D., & Karam, L. J.
- Affiliations
- Arizona State University (US)
- ORCID
- Karam, Lina J.
- Venue
- 2009 International Workshop on Quality of Multimedia Experience
- Publisher
- IEEE
- Published
- 2009-07
- Pages
- 87-91
- OpenAlex
- W2162220380
- Cited by
- 208
- References
- 14
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 15 · 2023: 26 · 2022: 17 · 2021: 14 · 2020: 20 · 2019: 12 · 2018: 9 · 2017: 11 · 2016: 14 · 2015: 12 · 2014: 13 · 2013: 12 · 2012: 11
- FWCI
- 5.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Quality of Multimedia Experience (QoMEX) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- no-reference image quality assessment, blur detection, cumulative probability, human visual system, perceptual image quality, visual attention, computational complexity
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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.
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