An image quality assessment metric based contourlet

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

An image quality assessment metric based contourlet

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

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2008, pp. 1172 - 1175
Issue Date:: 2008-12-01

Closed Access

	Filename	Description	Size
	2011001284OK.pdf		359.76 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, W	en_US
dc.contributor.author	Gao, X	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	Proceedings - International Conference on Image Processing, ICIP, 2008, pp. 1172 - 1175	en_US
dc.identifier.isbn	1424417643	en_US
dc.identifier.isbn	9781424417643	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17292
dc.description.abstract	In reduced-reference (RR) image quality assessment (IQA), the visual quality of distorted images is evaluated with only partial information extracted from original images. In this paper, by considering the information of textures and directions during image distortion, we propose a new reduced-reference IQA metric to calculate the diversifications based on contourlet transform. Experimental results illustrate that even with low data rate, the presented metric has still good consistency with the subjective perception. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2008.4711969	en_US
dc.title	An image quality assessment metric based contourlet	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	San Diego, CA	en_US
dc.location.activity	New York, NY, USA
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:

In reduced-reference (RR) image quality assessment (IQA), the visual quality of distorted images is evaluated with only partial information extracted from original images. In this paper, by considering the information of textures and directions during image distortion, we propose a new reduced-reference IQA metric to calculate the diversifications based on contourlet transform. Experimental results illustrate that even with low data rate, the presented metric has still good consistency with the subjective perception. © 2008 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/17292