The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images
Abstract
Contrast is a fundamental attribute of images that plays an important role in human visual perception of image quality. With numerous approaches proposed to enhance image contrast, much less work has been dedicated to automatic quality assessment of contrast changed images. Existing approaches rely on global statistics to estimate contrast quality. Here we propose a novel local patch-based objective quality assessment method using an adaptive representation of local patch structure, which allows us to decompose any image patch into its mean intensity, signal strength and signal structure components and then evaluate their perceptual distortions in different ways. A unique feature that differentiates the proposed method from previous contrast quality models is the capability to produce a local contrast quality map, which predicts local quality variations over space and may be employed to guide contrast enhancement algorithms. Validations based on four publicly available databases show that the proposed patch-based contrast quality index (PCQI) method provides accurate predictions on the human perception of contrast variations.
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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
Self-citationMultiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · cited 1×
“We compare the proposed method with both classical and state-of-the-art FR quality assessment algorithms, including PSNR, SSIM [6], MS-SSIM [10], IW-SSIM [11], GSIM [12], FSIM [13], GMSD [14], VSI [15] and VIF [16].”
MethodSelf-citationFSIM: 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
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
Xue, W., Zhang, L., Mou, X., & Bovik, A. C. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2293423 · Cited by 1,697
Information content weighting for perceptual image quality assessment
Wang, Z., & Li, Q. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2092435 · Cited by 1,304
Self-citationPeli, E. (1990) · Journal of the Optical Society of America A · doi:10.1364/josaa.7.002032 · Cited by 1,298
VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment
Zhang, L., Shen, Y., & Li, H. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2346028 · Cited by 1,029
TID2008 – A database for evaluation of full-reference visual quality assessment metrics
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Cited by
Color balance and fusion for underwater image enhancement
Ancuti, C. O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2759252 · Cited by 1,357
Reversible data hiding: Advances in the past two decades
Shi, Y.-Q., Li, X., Zhang, X., Wu, H.-T., & Ma, B. (2016) · IEEE Access · doi:10.1109/access.2016.2573308 · Cited by 594
Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends
Zhou, J., Yang, T., & Zhang, W. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03767-y · Cited by 149
Metadata
- Authors
- Wang, S., Ma, K., Yeganeh, H., Wang, Z., & Lin, W.
- Venue
- IEEE Signal Processing Letters
- Published
- 2015-12
- Volume
- 22
- Issue
- 12
- Pages
- 2387-2390
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W1644173899
- Cited by
- 444
- References
- 16
- FWCI
- 17.04
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, S., Ma, K., Yeganeh, H., Wang, Z., & Lin, W. (2015). A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images. IEEE Signal Processing Letters. https://doi.org/10.1109/lsp.2015.2487369
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