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Color separation for improved perceived image quality in terms of graininess and gamut
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7305-956X
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
2017 (English)In: Color Research and Application, ISSN 0361-2317, E-ISSN 1520-6378, Vol. 42, no 4, 486-497 p.Article in journal (Refereed) Published
Abstract [en]

Multi-channel printing employs additional inks to improve the perceived image quality by reducing the graininess and augmenting the printer gamut. It also requires a color separation that deals with the one-to-many mapping problem imposed when using more than three inks. The proposed separation model incorporates a multilevel halftoning algorithm, reducing the complexity of the print characterization by grouping inks of similar hues in the same channel. In addition, a cost function is proposed that weights selected factors influencing the print and perceived image quality, namely color accuracy, graininess and ink consumption. The graininess perception is qualitatively assessed using S-CIELAB, a spatial low-pass filtering mimicking the human visual system. By applying it to a large set of samples, a generalized prediction quantifying the perceived graininess is carried out and incorporated as a criterion in the color separation. The results of the proposed model are compared with the separation giving the best colorimetric match, showing improvements in the perceived image quality in terms of graininess at a small cost of color accuracy and ink consumption. (c) 2016 Wiley Periodicals, Inc.

Place, publisher, year, edition, pages
WILEY , 2017. Vol. 42, no 4, 486-497 p.
Keyword [en]
color reproduction; color separation; multi-channel printing; image quality; graininess
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-138871DOI: 10.1002/col.22105ISI: 000402823700010OAI: oai:DiVA.org:liu-138871DiVA: diva2:1115977
Available from: 2017-06-27 Created: 2017-06-27 Last updated: 2017-06-27

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Zitinski Elias, PaulaNyström, DanielGooran, Sasan
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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