A nonparametric-test-based structural similarity measure for digital images

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dc.contributor.author Wang, Haiyan
dc.contributor.author Maldonado, Diego M.
dc.contributor.author Silwal, Sharad Deep
dc.date.accessioned 2011-10-13T14:31:38Z
dc.date.available 2011-10-13T14:31:38Z
dc.date.issued 2011-10-13
dc.identifier.uri http://hdl.handle.net/2097/12368
dc.description.abstract In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures,such as the mean squared error (MSE) and peak signal to noise ratio (PSNR), ignore the spatial information (e.g. redundancy) contained in natural images, which can lead to an inconsistent similarity evaluation from the human visual perception. Recently, a structural similarity measure (SSIM), that quantifies image fidelity through estimation of local correlations scaled by local brightness and contrast comparisons, was introduced by Wang et al. [2004]. This correlation-based SSIM outperforms MSE in the similarity assessment of natural images. However, as correlation only measures linear dependence, distortions from multiple sources or nonlinear image processing such as nonlinear filtering can cause SSIM to under or overestimate the true structural similarity. In this article, we propose a new similarity measure that replaces the correlation and contrast comparisons of SSIM by a term obtained from a nonparametric test that has superior power to capture general dependence, including linear and nonlinear dependence in the conditional mean regression function as a special case. The new similarity measure applied to images from noise contamination, filtering, and watermarking, provides a more consistent image structural fidelity measure than commonly used measures. en_US
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S0167947311001502 en_US
dc.subject Image processing en_US
dc.subject Nonparametric hypothesis testing en_US
dc.subject Image structural similarity en_US
dc.subject Digital image watermarking en_US
dc.title A nonparametric-test-based structural similarity measure for digital images en_US
dc.type Article (author version) en_US
dc.date.published 2011 en_US
dc.citation.doi doi:10.1016/j.csda.2011.04.021 en_US
dc.citation.epage 2936 en_US
dc.citation.issue 11 en_US
dc.citation.jtitle Computational Statistics & Data Analysis en_US
dc.citation.spage 2925 en_US
dc.citation.volume 55 en_US
dc.contributor.authoreid hwang en_US
dc.contributor.authoreid dmaldona en_US
dc.contributor.authoreid sharad en_US

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