Local structure divergence index for image quality assessment

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

Local structure divergence index for image quality assessment

Gao, F Tao, D

Li, X Gao, X He, L

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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, 7667 LNCS (PART 5), pp. 337 - 344
Issue Date:: 2012-11-19

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Field	Value	Language
dc.contributor.author	Gao, F	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.contributor.author	He, L	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, 7667 LNCS (PART 5), pp. 337 - 344	en_US
dc.identifier.isbn	9783642344992	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32338
dc.identifier.uri	http://hdl.handle.net/10453/22906
dc.description.abstract	Image quality assessment (IQA) algorithms are important for image-processing systems. And structure information plays a significant role in the development of IQA metrics. In contrast to existing structure driven IQA algorithms that measure the structure information using the normalized image or gradient amplitudes, we present a new Local Structure Divergence (LSD) index based on the local structures contained in an image. In particular, we exploit the steering kernels to describe local structures. Afterward, we estimate the quality of a given image by calculating the symmetric Kullback-Leibler divergence (SKLD) between kernels of the reference image and the distorted image. Experimental results on the LIVE database II show that LSD performs consistently with the human perception with a high confidence, and outperforms representative structure driven IQA metrics across various distortions. © 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	10.1007/978-3-642-34500-5_40	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Local structure divergence index for image quality assessment	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 5	en_US
utslib.citation.volume	7667 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 5	en_US
pubs.publication-status	Published	en_US
pubs.volume	7667 LNCS	en_US

Abstract:

Image quality assessment (IQA) algorithms are important for image-processing systems. And structure information plays a significant role in the development of IQA metrics. In contrast to existing structure driven IQA algorithms that measure the structure information using the normalized image or gradient amplitudes, we present a new Local Structure Divergence (LSD) index based on the local structures contained in an image. In particular, we exploit the steering kernels to describe local structures. Afterward, we estimate the quality of a given image by calculating the symmetric Kullback-Leibler divergence (SKLD) between kernels of the reference image and the distorted image. Experimental results on the LIVE database II show that LSD performs consistently with the human perception with a high confidence, and outperforms representative structure driven IQA metrics across various distortions. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/22906