An efficient color quantization based on generic roughness measure

Yue, XD; Miao, DQ; Cao, LB; Wu, Q; Chen, YF

An efficient color quantization based on generic roughness measure

Yue, XD Miao, DQ Cao, LB

Wu, Q

Chen, YF

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2014, 47 (4), pp. 1777 - 1789
Issue Date:: 2014-04-01

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Field	Value	Language
dc.contributor.author	Yue, XD	en_US
dc.contributor.author	Miao, DQ	en_US
dc.contributor.author	Cao, LB https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Chen, YF	en_US
dc.date.issued	2014-04-01	en_US
dc.identifier.citation	Pattern Recognition, 2014, 47 (4), pp. 1777 - 1789	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34969
dc.description.abstract	Color quantization is a process to compress image color space while minimizing visual distortion. The quantization based on preclustering has low computational complexity but cannot guarantee quantization precision. The quantization based on postclustering can produce high quality quantization results. However, it has to traverse image pixels iteratively and suffers heavy computational burden. Its computational complexity was not reduced although the revised versions have improved the precision. In the work of color quantization, balancing quantization quality and quantization complexity is always a challenging point. In this paper, a two-stage quantization framework is proposed to achieve this balance. In the first stage, high-resolution color space is initially compressed to a condensed color space by thresholding roughness indices. Instead of linear compression, we propose generic roughness measure to generate the delicate segmentation of image color. In this way, it causes less distortion to the image. In the second stage, the initially compressed colors are further clustered to a palette using Weighted Rough K-means to obtain final quantization results. Our objective is to design a postclustering quantization strategy at the color space level rather than the pixel level. Applying the quantization in the precisely compressed color space, the computational cost is greatly reduced; meanwhile, the quantization quality is maintained. The substantial experimental results validate the high efficiency of the proposed quantization method, which produces high quality color quantization while possessing low computational complexity. © 2013 Elsevier Ltd.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2013.11.017	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An efficient color quantization based on generic roughness measure	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	47	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	47	en_US

Abstract:

Color quantization is a process to compress image color space while minimizing visual distortion. The quantization based on preclustering has low computational complexity but cannot guarantee quantization precision. The quantization based on postclustering can produce high quality quantization results. However, it has to traverse image pixels iteratively and suffers heavy computational burden. Its computational complexity was not reduced although the revised versions have improved the precision. In the work of color quantization, balancing quantization quality and quantization complexity is always a challenging point. In this paper, a two-stage quantization framework is proposed to achieve this balance. In the first stage, high-resolution color space is initially compressed to a condensed color space by thresholding roughness indices. Instead of linear compression, we propose generic roughness measure to generate the delicate segmentation of image color. In this way, it causes less distortion to the image. In the second stage, the initially compressed colors are further clustered to a palette using Weighted Rough K-means to obtain final quantization results. Our objective is to design a postclustering quantization strategy at the color space level rather than the pixel level. Applying the quantization in the precisely compressed color space, the computational cost is greatly reduced; meanwhile, the quantization quality is maintained. The substantial experimental results validate the high efficiency of the proposed quantization method, which produces high quality color quantization while possessing low computational complexity. © 2013 Elsevier Ltd.

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http://hdl.handle.net/10453/34969