Image quality assessment-a multiscale geometric analysis-based framework and examples

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

Image quality assessment-a multiscale geometric analysis-based framework and examples

Gao, X Lu, W Tao, D

Li, X

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Publication Type:: Chapter
Citation:: Handbook of Natural Computing, 2012, 1-4 pp. 376 - 399
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Lu, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Handbook of Natural Computing, 2012, 1-4 pp. 376 - 399	en_US
dc.identifier.isbn	9783540929093	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119453
dc.description.abstract	© Springer-Verlag Berlin Heidelberg 2012. All rights are reserved. This chapter is about objective image quality assessment (IQA), which has been recognized as an effective and efficient way to predict the visual quality of distorted images. Basically, IQA has three different dependent degrees on original images, namely, full-reference (FR), noreference (NR), and reduced-reference (RR). To begin with, we introduce the fundamentals of IQA and give a broad treatment of the state-of-the-art. We focus on a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), andWeber's law of just noticeable difference (JND). Thorough empirical studies were carried out using the laboratory for image and video engineering (LIVE) database against subjective mean opinion score (MOS), and these demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results well reflect the visual quality of the images.	en_US
dc.relation.ispartof	Handbook of Natural Computing	en_US
dc.relation.isbasedon	10.1007/978-3-540-92910-9_11	en_US
dc.title	Image quality assessment-a multiscale geometric analysis-based framework and examples	en_US
dc.type	Chapter
utslib.citation.volume	1-4	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.publication-status	Published	en_US
pubs.volume	1-4	en_US

Abstract:

© Springer-Verlag Berlin Heidelberg 2012. All rights are reserved. This chapter is about objective image quality assessment (IQA), which has been recognized as an effective and efficient way to predict the visual quality of distorted images. Basically, IQA has three different dependent degrees on original images, namely, full-reference (FR), noreference (NR), and reduced-reference (RR). To begin with, we introduce the fundamentals of IQA and give a broad treatment of the state-of-the-art. We focus on a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), andWeber's law of just noticeable difference (JND). Thorough empirical studies were carried out using the laboratory for image and video engineering (LIVE) database against subjective mean opinion score (MOS), and these demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results well reflect the visual quality of the images.

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http://hdl.handle.net/10453/119453