The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
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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Cites
Image quality assessment: from error visibility to structural similarity
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Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104
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Sheikh, H. R., Bovik, A. C., & de Veciana, G. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859389 · Cited by 1,399
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Cited by
Sorted by citations
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Moorthy, A. K., & Bovik, A. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2147325 · Cited by 1,763
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
Saad, M. A., Bovik, A. C., & Charrier, C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2191563 · Cited by 1,750
Blind/Referenceless Image Spatial Quality Evaluator
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2011) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2011.6190099 · Cited by 446
Seven Challenges in Image Quality Assessment: Past, Present, and Future Research
Chandler, D. M. (2013) · ISRN Signal Processing · doi:10.1155/2013/905685 · Cited by 427
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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