Color fractal structure model for reduced-reference colorful image quality assessment

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

Color fractal structure model for reduced-reference colorful image quality assessment

He, L Wang, D Li, X Tao, D

Gao, X Gao, F

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7664 LNCS (PART 2), pp. 401 - 408
Issue Date:: 2012-11-19

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Field	Value	Language
dc.contributor.author	He, L	en_US
dc.contributor.author	Wang, D	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Gao, F	en_US
dc.date.issued	2012-11-19	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7664 LNCS (PART 2), pp. 401 - 408	en_US
dc.identifier.isbn	9783642344800	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22904
dc.description.abstract	Developing reduced reference image quality assessment (RR-IQA) plays a vital role in dealing with the prediction of the visual quality of distorted images. However, most of existing methods fail to take color information into consideration, although the color distortion is significant for the increasing color images. To solve the aforementioned problem, this paper proposed a novel IQA method which focuses on the color distortion. In particular, we extract color features based on the model of color fractal structure. Then the color and structure features are mapped into visual quality using the support vector regression. Experimental results on the LIVE II database demonstrate that the proposed method has a good consistency with the human perception especially on images with color distortion. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-34481-7_49	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Color fractal structure model for reduced-reference colorful image quality assessment	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	7664 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	7664 LNCS	en_US

Abstract:

Developing reduced reference image quality assessment (RR-IQA) plays a vital role in dealing with the prediction of the visual quality of distorted images. However, most of existing methods fail to take color information into consideration, although the color distortion is significant for the increasing color images. To solve the aforementioned problem, this paper proposed a novel IQA method which focuses on the color distortion. In particular, we extract color features based on the model of color fractal structure. Then the color and structure features are mapped into visual quality using the support vector regression. Experimental results on the LIVE II database demonstrate that the proposed method has a good consistency with the human perception especially on images with color distortion. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/22904