No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model

Lu, Y; Xie, F; Liu, T; Jiang, Z; Tao, D

No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model

Lu, Y Xie, F Liu, T Jiang, Z Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2015, 22 (10), pp. 1811 - 1815
Issue Date:: 2015-10-01

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Field	Value	Language
dc.contributor.author	Lu, Y	en_US
dc.contributor.author	Xie, F	en_US
dc.contributor.author	Liu, T	en_US
dc.contributor.author	Jiang, Z	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2015-10-01	en_US
dc.identifier.citation	IEEE Signal Processing Letters, 2015, 22 (10), pp. 1811 - 1815	en_US
dc.identifier.issn	1070-9908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122032
dc.description.abstract	© 2015 IEEE. Multiple distortion assessment is a big challenge in image quality assessment (IQA). In this letter, a no reference IQA model for multiply-distorted images is proposed. The features, which are sensitive to each distortion type even in the presence of other distortions, are first selected from three kinds of NSS features. An improved Bag-of-Words (BoW) model is then applied to encode the selected features. Lastly, a simple yet effective linear combination is used to map the image features to the quality score. The combination weights are obtained through lasso regression. A series of experiments show that the feature selection strategy and the improved BoW model are effective in improving the accuracy of quality prediction for multiple distortion IQA. Compared with other algorithms, the proposed method delivers the best result for multiple distortion IQA.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140102164
dc.relation	http://purl.org/au-research/grants/arc/FT130101457
dc.relation	http://purl.org/au-research/grants/arc/LP140100569
dc.relation.ispartof	IEEE Signal Processing Letters	en_US
dc.relation.isbasedon	10.1109/LSP.2015.2436908	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	22	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	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	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

© 2015 IEEE. Multiple distortion assessment is a big challenge in image quality assessment (IQA). In this letter, a no reference IQA model for multiply-distorted images is proposed. The features, which are sensitive to each distortion type even in the presence of other distortions, are first selected from three kinds of NSS features. An improved Bag-of-Words (BoW) model is then applied to encode the selected features. Lastly, a simple yet effective linear combination is used to map the image features to the quality score. The combination weights are obtained through lasso regression. A series of experiments show that the feature selection strategy and the improved BoW model are effective in improving the accuracy of quality prediction for multiple distortion IQA. Compared with other algorithms, the proposed method delivers the best result for multiple distortion IQA.

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http://hdl.handle.net/10453/122032