Sparse representation for blind image quality assessment

He, L; Tao, D; Li, X; Gao, X

Sparse representation for blind image quality assessment

He, L Tao, D

Li, X Gao, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 1146 - 1153
Issue Date:: 2012-10-01

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Field	Value	Language
dc.contributor.author	He, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Gao, X	en_US
dc.date.issued	2012-10-01	en_US
dc.identifier.citation	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 1146 - 1153	en_US
dc.identifier.isbn	9781467312264	en_US
dc.identifier.issn	1063-6919	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22947
dc.description.abstract	Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: 1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; 2) they are sensitive to different datasets; and 3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics. © 2012 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120103730
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/CVPR.2012.6247795	en_US
dc.title	Sparse representation for blind image quality assessment	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Providence, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: 1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; 2) they are sensitive to different datasets; and 3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics. © 2012 IEEE.

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http://hdl.handle.net/10453/22947