Natural scene statistics based blind image quality assessment in spatial domain
We propose a natural scene statistic based quality assessment model Refer- enceless Image Spatial QUality Evaluator (RISQUE) which extracts marginal statistics of local normalized luminance signals and measures 'un-naturalness' of the distorted image based on measured deviation of them. We...
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ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2011-05-32202015-09-20T17:02:37ZNatural scene statistics based blind image quality assessment in spatial domainMittal, AnishImage quality assessmentNo reference imagesSpatial domainNatural scene statisticsReferenceless imageVideo quality assessmentWe propose a natural scene statistic based quality assessment model Refer- enceless Image Spatial QUality Evaluator (RISQUE) which extracts marginal statistics of local normalized luminance signals and measures 'un-naturalness' of the distorted image based on measured deviation of them. We also model distribution of pairwise products of adjacent normalized luminance signals providing us with orientation distortion information. Although multi-scale, the model is defined in the space domain avoiding costly frequency or wavelet transforms. The frame work is simple, fast, human perception based and shown to perform statistically better than other proposed no reference algorithms and full reference structural similarity index(SSIM).text2011-08-05T16:17:58Z2011-08-05T16:17:58Z2011-052011-08-05May 20112011-08-05T16:18:12Zthesisapplication/pdfhttp://hdl.handle.net/2152/ETD-UT-2011-05-32202152/ETD-UT-2011-05-3220eng |
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Others
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Image quality assessment No reference images Spatial domain Natural scene statistics Referenceless image Video quality assessment |
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Image quality assessment No reference images Spatial domain Natural scene statistics Referenceless image Video quality assessment Mittal, Anish Natural scene statistics based blind image quality assessment in spatial domain |
description |
We propose a natural scene statistic based quality assessment model Refer- enceless Image Spatial QUality Evaluator (RISQUE) which extracts marginal statistics of local normalized luminance signals and measures 'un-naturalness' of the distorted image based on measured deviation of them. We also model distribution of pairwise products of adjacent normalized luminance signals providing us with orientation distortion information. Although multi-scale, the model is defined in the space domain avoiding costly frequency or wavelet transforms. The frame work is simple, fast, human perception based and shown to perform statistically better than other proposed no reference algorithms and full reference structural similarity index(SSIM). === text |
author |
Mittal, Anish |
author_facet |
Mittal, Anish |
author_sort |
Mittal, Anish |
title |
Natural scene statistics based blind image quality assessment in spatial domain |
title_short |
Natural scene statistics based blind image quality assessment in spatial domain |
title_full |
Natural scene statistics based blind image quality assessment in spatial domain |
title_fullStr |
Natural scene statistics based blind image quality assessment in spatial domain |
title_full_unstemmed |
Natural scene statistics based blind image quality assessment in spatial domain |
title_sort |
natural scene statistics based blind image quality assessment in spatial domain |
publishDate |
2011 |
url |
http://hdl.handle.net/2152/ETD-UT-2011-05-3220 |
work_keys_str_mv |
AT mittalanish naturalscenestatisticsbasedblindimagequalityassessmentinspatialdomain |
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1716821980075786240 |