A novel metric based on MCA for image quality

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

A novel metric based on MCA for image quality

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

Li, X

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Publication Type:: Journal Article
Citation:: International Journal of Wavelets, Multiresolution and Information Processing, 2011, 9 (5), pp. 743 - 757
Issue Date:: 2011-09-01

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Field	Value	Language
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.contributor.author	Li, X	en_US
dc.date.issued	2011-09-01	en_US
dc.identifier.citation	International Journal of Wavelets, Multiresolution and Information Processing, 2011, 9 (5), pp. 743 - 757	en_US
dc.identifier.issn	0219-6913	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28825
dc.description.abstract	Considering that the Human Visual System (HVS) has different perceptual characteristics for different morphological components, a novel image quality metric is proposed by incorporating Morphological Component Analysis (MCA) and HVS, which is capable of assessing the image with different kinds of distortion. Firstly, reference and distorted images are decomposed into linearly combined texture and cartoon components by MCA respectively. Then these components are turned into perceptual features by Just Noticeable Difference (JND) which integrates masking features, luminance adaptation and Contrast Sensitive Function (CSF). Finally, the discrimination between reference and distorted images perceptual features is quantified using a pooling strategy before the final image quality is obtained. Experimental results demonstrate that the performance of the proposed prevails over some existing methods on LIVE database II. © 2011 World Scientific Publishing Company.	en_US
dc.relation.ispartof	International Journal of Wavelets, Multiresolution and Information Processing	en_US
dc.relation.isbasedon	10.1142/S0219691311004298	en_US
dc.subject.classification	Numerical & Computational Mathematics	en_US
dc.title	A novel metric based on MCA for image quality	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	9	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	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	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

Considering that the Human Visual System (HVS) has different perceptual characteristics for different morphological components, a novel image quality metric is proposed by incorporating Morphological Component Analysis (MCA) and HVS, which is capable of assessing the image with different kinds of distortion. Firstly, reference and distorted images are decomposed into linearly combined texture and cartoon components by MCA respectively. Then these components are turned into perceptual features by Just Noticeable Difference (JND) which integrates masking features, luminance adaptation and Contrast Sensitive Function (CSF). Finally, the discrimination between reference and distorted images perceptual features is quantified using a pooling strategy before the final image quality is obtained. Experimental results demonstrate that the performance of the proposed prevails over some existing methods on LIVE database II. © 2011 World Scientific Publishing Company.

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http://hdl.handle.net/10453/28825