The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments
Abstract
Blur is a key determinant in the perception of image quality. Generally, blur causes spread of edges, which leads to shape changes in images. Discrete orthogonal moments have been widely studied as effective shape descriptors. Intuitively, blur can be represented using discrete moments since noticeable blur affects the magnitudes of moments of an image. With this consideration, this paper presents a blind image blur evaluation algorithm based on discrete Tchebichef moments. The gradient of a blurred image is first computed to account for the shape, which is more effective for blur representation. Then the gradient image is divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape. The energy of a block is computed as the sum of squared non-DC moment values. Finally, the proposed image blur score is defined as the variance-normalized moment energy, which is computed with the guidance of a visual saliency model to adapt to the characteristic of human visual system. The performance of the proposed method is evaluated on four public image quality databases. The experimental results demonstrate that our method can produce blur scores highly consistent with subjective evaluations. It also outperforms the state-of-the-art image blur metrics and several general-purpose no-reference quality metrics.
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Cites
Image quality assessment: from error visibility to structural similarity
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Making a “Completely Blind” Image Quality Analyzer
Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766
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
FSIM: A Feature Similarity Index for Image Quality Assessment
Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2109730 · Cited by 5,203
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881959 · Cited by 2,966
Most apparent distortion: full-reference image quality assessment and the role of strategy
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Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
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Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
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Information content weighting for perceptual image quality assessment
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Cited by
No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization
Gu, K., Lin, W., Zhai, G., Yang, X., Zhang, W., & Chen, C. W. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2575544 · Cited by 371
Self-citationBlind omnidirectional image quality assessment with viewport oriented graph convolutional networks
Xu, J., Zhou, W., & Chen, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3015186 · Cited by 166
Deep neural network for blind visual quality assessment of 4K content
Lu, W., Sun, W., Min, X., Zhu, W., Zhou, Q., He, J., Wang, Q., Zhang, Z., Wang, T., & Zhai, G. (2023) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2022.3221689 · Cited by 60
Metadata
- Authors
- Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., & Kot, A. C.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2016-01
- Volume
- 46
- Issue
- 1
- Pages
- 39-50
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W1978598038
- Cited by
- 270
- References
- 44
- FWCI
- 31.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., & Kot, A. C. (2016). No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tcyb.2015.2392129
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