Diverging Color Maps for Scientific Visualization
One of the most fundamental features of scientific visualization is the process of mapping scalar values to colors. This process allows us to view scalar fields by coloring surfaces and volumes. Unfortunately, the majority of scientific visualization tools still use a color map that is famous for its ineffectiveness: the rainbow color map. This color map, which naïvely sweeps through the most saturated colors, is well known for its ability to obscure data, introduce artifacts, and confuse users. Although many alternate color maps have been proposed, none have achieved widespread adoption by the visualization community for scientific visualization. This paper explores the use of diverging color maps (sometimes also called ratio, bipolar, or double-ended color maps) for use in scientific visualization, provides a diverging color map that generally performs well in scientific visualization applications, and presents an algorithm that allows users to easily generate their own customized color maps.
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- 2.Brewer, C.A.: Designing better MAPS: A Guide for GIS Users. ESRI Press (2005) ISBN 1-58948-089-9Google Scholar
- 4.Rheingans, P.: Task-based color scale design. In: Proceedings of Applied Image and Pattern Recognition 1999, pp. 35–43 (1999)Google Scholar
- 5.Ware, C.: Information Visualization: Perception for Design, 2nd edn. Morgan Kaufmann, San Francisco (2004)Google Scholar
- 7.Mullen, K.T.: The contrast sensitivity of human colour vision to red–green and blue–yellow chromatic gratings. The Journal of Physiology 359, 381–400 (1985)Google Scholar
- 10.Rogowitz, B.E., Treinish, L.A., Bryson, S.: How not to lie with visualization. Computers in Physics 10, 268–273 (1996)Google Scholar
- 11.Stone, M.C.: A Field Guide to Digital Color. A K Peters (2003) 1-56881-161-6Google Scholar
- 12.Wyszecki, G., Stiles, W.: Color Science: Concepts and Methods, Quantitative Data and Formulae. John Wiley & Sons, Inc., Chichester (1982)Google Scholar
- 13.Fortner, B., Meyer, T.E.: Number by Colors: a Guide to Using Color to Understand Technical Data. Springer, Heidelberg (1997)Google Scholar
- 15.Hardin, C., Maffi, L. (eds.): Color categories in thought and language. Cambridge University Press, Cambridge (1997)Google Scholar