Assessment of random-noise contamination in digital images via testing on wavelet coefficients

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dc.contributor.author Silwal, Sharad Deep
dc.contributor.author Wang, Haiyan
dc.contributor.author Maldonado, Diego M.
dc.date.accessioned 2013-06-12T21:32:56Z
dc.date.available 2013-06-12T21:32:56Z
dc.date.issued 2013-06-12
dc.identifier.uri http://hdl.handle.net/2097/15907
dc.description.abstract Full-reference image quality assessment methods seek to measure visual similarity between two images (in practice, one original and the other its altered version). It has been established that traditional methods, such as Mean Square Error and Peak Signal-to-Noise Ratio poorly mimic the human visual system and much of the recent research in image quality assessment has been directed toward developing image similarity measures that are more consistent with assessments from human observers. Some extensively tested popular methods in this regard are Visual Image Fidelity (VIF), Structure Similarity Index (SSIM) and its variants Multi-scale Structure Similarity Index (MS-SSIM) and Information Content Weighted Multi-scale Structure Similarity Index (IW-SSIM). However, experiments show that these methods may produce drastically different similarity indices for different images contaminated with the same source of random noise. In this article, we propose a new full-reference image quality assessment method, namely, Wavelet-based Non-parametric Structure Similarity Index (WNPSSIM), specifically designed to detect visual similarity between images contaminated with all sorts of random noises. WNPSSIM is based on a rank test of the hypothesis of identical images conducted on the wavelet domain. Our experimental comparisons demonstrate that WNPSSIM provides similar ranking as MS-SSIM, IW-SSIM and VIF for images contaminated with different random noises in general though the methodology is very different. In addition, WNPSSIM corrects the aforementioned shortcoming of assigning sharply different similarity indices for different images contaminated with the same source of random noise. en_US
dc.language.iso en_US en_US
dc.relation.uri http://intlpress.com/site/pub/pages/journals/items/sii/content/vols/0006/0001/00026395/index.html en_US
dc.rights First published in Statistics and Its Interface in Volume 6 Issue 1, 2013, published by International Press. © International Press of Boston, Inc. en_US
dc.subject Image structure similarity en_US
dc.subject Non-parametric hypothesis testing en_US
dc.subject Full-reference en_US
dc.subject Human visual system (HVS) en_US
dc.subject Discrete wavelet transform (DWT) en_US
dc.title Assessment of random-noise contamination in digital images via testing on wavelet coefficients en_US
dc.type Article (publisher version) en_US
dc.date.published 2013 en_US
dc.citation.epage 135 en_US
dc.citation.issue 1 en_US
dc.citation.jtitle Statistics and Its Interface en_US
dc.citation.spage 117 en_US
dc.citation.volume 6 en_US
dc.contributor.authoreid hwang en_US
dc.contributor.authoreid dmaldona en_US


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