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Color-weak compensation using local affine isometry based on discrimination threshold matching
NTT Solut Lab, Japan.
Chuo University, Japan.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0001-7557-4904
Chuo University, Japan.
2015 (English)In: Optical Society of America. Journal A: Optics, Image Science, and Vision, ISSN 1084-7529, E-ISSN 1520-8532, Vol. 32, no 11, 2093-2103 p.Article in journal (Refereed) Published
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Abstract [en]

We develop algorithms for color-weak compensation and color-weak simulation based on Riemannian geometry models of color spaces. The objective function introduced measures the match of color discrimination thresholds of average normal observers and a color-weak observer. The developed matching process makes use of local affine maps between color spaces of color-normal and color-weak observers. The method can be used to generate displays of images that provide color-normal and color-weak observers with a similar color difference experience. It can also be used to simulate the perception of a color-weak observer for color-normal observers. We also introduce a new database of measurements of color discrimination threshold data for color-normal and color-weak observers obtained at different lightness levels in CIELUV space. The compensation methods include compensations of chromaticity using local affine maps between chromaticity planes of color-normal and color-weak observers, and one-dimensional (1D) compensation on lightness. We describe how to determine correspondences between the origins of local coordinates in color spaces of color-normal and color-weak observers using a neighborhood expansion method. After matching the origins of the two coordinate systems, a local affine map is estimated by solving a nonlinear equation, or singular-value-decomposition (SVD). We apply the methods to natural images and evaluate their performance using the semantic differential (SD) method. (C) 2015 Optical Society of America

Place, publisher, year, edition, pages
OPTICAL SOC AMER , 2015. Vol. 32, no 11, 2093-2103 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-124139DOI: 10.1364/JOSAA.32.002093ISI: 000367201600021PubMedID: 26560924OAI: oai:DiVA.org:liu-124139DiVA: diva2:896773
Note

Funding Agencies|Institute of Science and Engineering, Chuo University; Japan Society for the Promotion of Science (JSPS) [23500156]; Swedish Foundation for Strategic Research [IIS11-0081]; Swedish Research Council [2014-6227]

Available from: 2016-01-22 Created: 2016-01-19 Last updated: 2017-11-30

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Lenz, Reiner

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