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Microscale halftone color image analysis: perspective of spectral color prediction modeling
Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.ORCID iD: 0000-0001-7387-6650
Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.ORCID iD: 0000-0002-0529-1009
2014 (English)In: Proceedings of SPIE - The International Society for Optical Engineering: Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications, San Francisco, California, United States: SPIE - International Society for Optical Engineering, 2014, Art. no. 901506- p.Conference paper, Published paper (Refereed)
Abstract [en]

A method has been proposed, whereby k-means clustering technique is applied to segment microscale single color halftone image into three components—solid ink, ink/paper mixed area and unprinted paper. The method has been evaluated using impact (offset) and non-impact (electro-photography) based single color prints halftoned by amplitude modulation (AM) and frequency modulation (FM) technique. The print samples have also included a range of variations in paper substrates. The colors of segmented regions have been analyzed in CIELAB color space to reveal the variations, in particular those present in mixed regions. The statistics of intensity distribution in the segmented areas have been utilized to derive expressions that can be used to calculate simple thresholds. However, the segmented results have been employed to study dot gain in comparison with traditional estimation technique using Murray-Davies formula. The performance of halftone reflectance prediction by spectral Murray-Davies model has been reported using estimated and measured parameters. Finally, a general idea has been proposed to expand the classical Murray-Davies model based on experimetal observations. Hence, the present study primarily presents the outcome of experimental efforts to characterize halftone print media interactions in respect to the color prediction models. Currently, most regression-based color prediction models rely on mathematical optimization to estimate the parameters using measured average reflectance of a large area compared to the dot size. While this general approach has been accepted as a useful tool, experimental investigations can enhance understanding of the physical processes and facilitate exploration of new modeling strategies. Furthermore, reported findings may help reduce the required number of samples that are printed and measured in the process of multichannel printer characterization and calibration.

Place, publisher, year, edition, pages
San Francisco, California, United States: SPIE - International Society for Optical Engineering, 2014. Art. no. 901506- p.
Keyword [en]
Color, modeling, halftoning, segmentation, dot gain, k-means, Murray Davies, CIE LAB
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-20314DOI: 10.1117/12.2037256ISI: 000333196800006Scopus ID: 2-s2.0-84894564647ISBN: 978-081949932-5 (print)OAI: oai:DiVA.org:miun-20314DiVA: diva2:667279
Conference
Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications San Francisco, California, USA, 3 - 5 February 2014
Projects
EU Marie Curie project (CP7.0)
Funder
EU, European Research Council, N-290154
Note

Manuscripts from this conference will appear as Proceedings of SPIE Volume 9015 on SPIE Digital Library the first day of the meeting.

Available from: 2013-11-26 Created: 2013-11-26 Last updated: 2015-01-02Bibliographically approved
In thesis
1. Image analysis approach for modeling color predictions in printing
Open this publication in new window or tab >>Image analysis approach for modeling color predictions in printing
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2014. 28 p.
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 108
National Category
Natural Sciences
Identifiers
urn:nbn:se:miun:diva-24030 (URN)978-91-87557-32-3 (ISBN)
Supervisors
Available from: 2015-01-02 Created: 2015-01-02 Last updated: 2015-01-02Bibliographically approved

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CiteExportLink to record
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Citation style
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