The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Blind Image Quality Estimation via Distortion Aggravation
Abstract
Traditional blind image quality assessment (IQA) measures generally predict quality from a sole distorted image directly. In this paper, we first introduce multiple pseudo reference images (MPRIs) by further degrading the distorted image in several ways and to certain degrees, and then compare the similarities between the distorted image and the MPRIs. Via such distortion aggravation, we can have some references to compare with, i.e., the MPRIs, and utilize the full-reference IQA framework to compute the quality. Specifically, we apply four types and five levels of distortion aggravation to deal with the commonly encountered distortions. Local binary pattern features are extracted to describe the similarities between the distorted image and the MPRIs. The similarity scores are then utilized to estimate the overall quality. More similar to a specific pseudo reference image (PRI) indicates closer quality to this PRI. Owning to the availability of the created multiple PRIs, we can reduce the influence of image content, and infer the image quality more accurately and consistently. Validation is conducted on four mainstream natural scene image and screen content image quality assessment databases, and the proposed method is comparable to or outperforms the state-of-the-art blind IQA measures. The MATLAB source code of the proposed measure will be publicly available.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
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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
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“To have an intuitive understanding of the above phenomenon, we select six NR measures, including DIIVINE [15], BLIINDS2 [16], BRISQUE [17], NFERM [18], IL-NIQE [19], BPRI [20], and six FR measures, including SSIM [21], MS-SSIM [22], VIF [23],”
MethodNo-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
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Image information and visual quality
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Most apparent distortion: full-reference image quality assessment and the role of strategy
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Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
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Cited by
Study of Subjective and Objective Quality Assessment of Audio-Visual Signals
Min, X., Zhai, G., Zhou, J., Farias, M. C. Q., & Bovik, A. C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2988148 · Cited by 328
Self-citationPerceptual video quality assessment: a survey
Min, X., Duan, H., Sun, W., Zhu, Y., & Zhai, G. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4133-3 · Cited by 218
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
Wang, J., Duan, H., Liu, J., Chen, S., Min, X., & Zhai, G. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-981-99-9119-8_5 · Cited by 80
Self-citationDeep 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
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Metadata
- Authors
- Min, X., Zhai, G., Gu, K., Liu, Y., & Yang, X.
- Venue
- IEEE Transactions on Broadcasting
- Published
- 2018-06
- Volume
- 64
- Issue
- 2
- Pages
- 508-517
- ISSN
- 0018-9316 (ISSN-L)
- 1557-9611
- OpenAlex
- W2794680924
- Cited by
- 508
- References
- 44
- FWCI
- 15.08
- Type
- journal article
- Language
- en
- Open access
- closed
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