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Blind image quality assessment through anisotropy
Open accessAbstract
We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.
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“Training based NR QA techniques have resulted in algorithms that perform at least as well as mean squared error, which has the benefit of a reference image [9], [10].”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [10]
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“In [11], blind image quality assessment indices that measure the anisotropy in the distorted image through the Renyi entropy are introduced. However, performance of this index has not been thoroughly evaluated on a large and comprehensive dataset.”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [11]
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Metadata
- Authors
- Gabarda, S., & Cristóbal, G.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Venue
- Journal of the Optical Society of America A
- Publisher
- Optica Publishing Group
- Published
- 2007-09-26
- Volume
- 24
- Issue
- 12
- Pages
- B42
- ISSN
- 1084-7529 (ISSN-L)
- 1520-8532
- OpenAlex
- W2119999314
- Cited by
- 301
- References
- 33
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 8 · 2023: 8 · 2022: 9 · 2021: 17 · 2020: 14 · 2019: 18 · 2018: 28 · 2017: 39 · 2016: 28 · 2015: 26 · 2014: 22 · 2013: 25 · 2012: 20
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Journal of the Optical Society of America A (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, other-oa)
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- no-reference image quality assessment, image quality index, anisotropy, Rényi entropy, pseudo-Wigner distribution, direction selectivity, image degradation, image classification
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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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