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Volume: 5 | Article ID: art00034
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Learning Color Appearance Models
E. Boldrin
P. Campadelli
R. Schettini
  DOI :  10.2352/CIC.1997.5.1.art00034  Published Online :  January 1997
Abstract

We present a method for faithfully approximating the Hunt94, LLAB and RLAB color appearance models by means of feed-forward neural networks trained with the error back-propagation algorithm. In particular we present experimental evidence that in eight “standard” viewing conditions the same network architecture is capable of learning quite satisfactorily the transformations performed by the three models.

Journal Title : Color and Imaging Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
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 DOI 10.2352/CIC.1997.5.1.art00034
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E. Boldrin, P. Campadelli, R. Schettini, "Learning Color Appearance Models"  in Proc. IS&T 5th Color and Imaging Conf.,  1997,  pp 173 - 176,  https://doi.org/10.2352/CIC.1997.5.1.art00034

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Copyright © Society for Imaging Science and Technology 1997
72010350
Color and Imaging Conference
color imaging conf
2166-9635
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2166-9635(19970101)1997:1L.173;1-
cic_v1997n1/splitsection34.xml
/ist/cic/1997/00001997/00000001/art00034
Articles
Learning Color Appearance Models
BoldrinE.
CampadelliP.
SchettiniR.
01011997
1997
1
173
176
1997
We present a method for faithfully approximating the Hunt94, LLAB and RLAB color appearance models by means of feed-forward neural networks trained with the error back-propagation algorithm. In particular we present experimental evidence that in eight “standard” viewing conditions the same network architecture is capable of learning quite satisfactorily the transformations performed by the three models.