A novel image quality metric based on morphological component analysis

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

A novel image quality metric based on morphological component analysis

Li, X He, L Lu, W Gao, X Tao, D

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Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2010, pp. 1449 - 1454
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Li, X	en_US
dc.contributor.author	He, L	en_US
dc.contributor.author	Lu, W	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2010, pp. 1449 - 1454	en_US
dc.identifier.isbn	9781424465880	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17277
dc.description.abstract	Due to that human eye has different perceptual characteristics for different morphological components, so a novel image quality metric is proposed by incorporating morphological component analysis (MCA) and human visual system (HVS), which is capable of assessing the image with different types of distortion. Firstly, reference and distorted images are decomposed into texture and cartoon components by MCA respectively. Then these components are changed into perceptual features by just noticeable difference (JND) which integrates masking features, luminance adaptation and contrast sensitive function (CSF). Finally, the difference between reference and distorted images' perceptual features is quantified using a pooling strategy, and then the final result of the image quality is obtained. Experimental results demonstrate that the performance of the metric prevail over some existing methods on LIVE database II. ©2010 IEEE.	en_US
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICSMC.2010.5642482	en_US
dc.title	A novel image quality metric based on morphological component analysis	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Istanbul, Turkey	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:

Due to that human eye has different perceptual characteristics for different morphological components, so a novel image quality metric is proposed by incorporating morphological component analysis (MCA) and human visual system (HVS), which is capable of assessing the image with different types of distortion. Firstly, reference and distorted images are decomposed into texture and cartoon components by MCA respectively. Then these components are changed into perceptual features by just noticeable difference (JND) which integrates masking features, luminance adaptation and contrast sensitive function (CSF). Finally, the difference between reference and distorted images' perceptual features is quantified using a pooling strategy, and then the final result of the image quality is obtained. Experimental results demonstrate that the performance of the metric prevail over some existing methods on LIVE database II. ©2010 IEEE.

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http://hdl.handle.net/10453/17277