Wavelet-based contourlet in quality evaluation of digital images

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

Wavelet-based contourlet in quality evaluation of digital images

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

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2008, 72 (1-3), pp. 378 - 385
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Lu, W	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Neurocomputing, 2008, 72 (1-3), pp. 378 - 385	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15057
dc.description.abstract	Feature extraction is probably the most important stage in image quality evaluation-effective features can well reflect the quality of digital images and vice versa. As a non-redundant sparse representation, contourlet transform can effectively reflect visual characteristics of images, and it can be employed to perceptually capture the difference between images. Motivated by this, this paper first proposes an objective reduced-reference image quality evaluation metric based on contourlet transform. Experiments demonstrate that this new objective metric achieves consistent image quality evaluation results with what gained by subjective evaluation. © 2008 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2007.12.031	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Wavelet-based contourlet in quality evaluation of digital images	en_US
dc.type	Journal Article
utslib.citation.volume	1-3	en_US
utslib.citation.volume	72	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive 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	1-3	en_US
pubs.publication-status	Published	en_US
pubs.volume	72	en_US

Abstract:

Feature extraction is probably the most important stage in image quality evaluation-effective features can well reflect the quality of digital images and vice versa. As a non-redundant sparse representation, contourlet transform can effectively reflect visual characteristics of images, and it can be employed to perceptually capture the difference between images. Motivated by this, this paper first proposes an objective reduced-reference image quality evaluation metric based on contourlet transform. Experiments demonstrate that this new objective metric achieves consistent image quality evaluation results with what gained by subjective evaluation. © 2008 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/15057