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Color transform analysis for microscale image segmentation to study halftone model parameters

Color transform analysis for microscale image segmentation to study halftone model parameters AbstractThis article presents a comprehensive study of 30color transforms to accurately segment images of halftoneprints and thus calculating the parameters of a color predictionmodel. The transforms are evaluated combiningthree metrics: the model accuracy,Otsu’s discriminant, andcorrelation coefficients of histograms. Hierarchical clusteranalysis is applied to determine the thresholds to segmentthe image histogram into paper, ink and mixed area.Among the 30 different transforms discussed in this article,21 channels are of 7 color space models (RGB, CMYK,CIELAB, HSV, YIQ, YCbCr, and XYZ) and the other 9 channelsare specially designed. Notable increase in model accuracyvalidates the segmentation accuracy and the necessityof choosing the appropriate transform. A set of 180halftone images of different print properties (such as paper,halftone, ink and printing technology) has been usedfor the evaluation. It is found that, the most appropriatetransform depends on the type of primary ink, but the correspondingtransforms in CMYK color space model haveshown consistent performance. CMYK-C, XYZ-Y and LAB-Bare the best transforms for Cyan, Magenta and Yellow inkcolor respectively. YIQ-I and HSV-S are good candidates if asingle transform is to be chosen for all primary ink colors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Computer Science de Gruyter

Color transform analysis for microscale image segmentation to study halftone model parameters

Rahaman, G M Atiqur; Islam, Md. Zahidul
Open Computer Science , Volume 6 (1): 20 – Jan 1, 2016
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Publisher
de Gruyter
Copyright
© 2016 G. M. Atiqur Rahaman and Md. Zahidul Islam
eISSN
2299-1093
DOI
10.1515/comp-2016-0013
Publisher site
See Article on Publisher Site

Abstract

AbstractThis article presents a comprehensive study of 30color transforms to accurately segment images of halftoneprints and thus calculating the parameters of a color predictionmodel. The transforms are evaluated combiningthree metrics: the model accuracy,Otsu’s discriminant, andcorrelation coefficients of histograms. Hierarchical clusteranalysis is applied to determine the thresholds to segmentthe image histogram into paper, ink and mixed area.Among the 30 different transforms discussed in this article,21 channels are of 7 color space models (RGB, CMYK,CIELAB, HSV, YIQ, YCbCr, and XYZ) and the other 9 channelsare specially designed. Notable increase in model accuracyvalidates the segmentation accuracy and the necessityof choosing the appropriate transform. A set of 180halftone images of different print properties (such as paper,halftone, ink and printing technology) has been usedfor the evaluation. It is found that, the most appropriatetransform depends on the type of primary ink, but the correspondingtransforms in CMYK color space model haveshown consistent performance. CMYK-C, XYZ-Y and LAB-Bare the best transforms for Cyan, Magenta and Yellow inkcolor respectively. YIQ-I and HSV-S are good candidates if asingle transform is to be chosen for all primary ink colors.

Journal

Open Computer Science – de Gruyter

Published: Jan 1, 2016

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