Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

Mittelstädt, Sebastian; Bernard, Jürgen; Schreck, Tobias; Steiger, Martin; Kohlhammer, Jörn; Keim, Daniel A.

Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

Files

091-095.pdf (1.02 MB)

Date

2014

Authors

Mittelstädt, Sebastian
Bernard, Jürgen
Schreck, Tobias
Steiger, Martin
Kohlhammer, Jörn
Keim, Daniel A.

Publisher

The Eurographics Association

Abstract

Color is one of the most effective visual variables since it can be combined with other mappings and encodeinformation without using any additional space on the display. An important example where expressing additionalvisual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity isdesirable in this application, because the user intuitively perceives clusters and relations among multi-dimensionaldata points. Many approaches use two-dimensional colormaps in their analysis, which are typically created byinterpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colorsare either saturated and discriminative but not perceptual linear or vice versa. A solution that combines bothadvantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in InformationVisualization according to our literature analysis. The method maps high-dimensional data points into the CIELABcolor space by maintaining the relative perceived distances of data points and color discrimination. In this paper,we generalize and extend the method of Kaski et al. to provide perceptual uniform color mapping for visual analysisof high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.

        @inproceedings{:10.2312/eurovisshort.20141163
https::/diglib.eg.org/handle/10.2312/eurovisshort.20141163.091-095
,
booktitle = {EuroVis - Short Papers
},
editor = {N. Elmqvist and M. Hlawitschka and J. Kennedy
},
title = {{Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis
}},
author = {Mittelstädt, Sebastian and 
Bernard, Jürgen and 
Schreck, Tobias and 
Steiger, Martin and 
Kohlhammer, Jörn and 
Keim, Daniel A.
},
year = {2014
},
publisher = {The Eurographics Association
},
ISBN = {978-3-905674-69-9
},
DOI = {/10.2312/eurovisshort.20141163
https://diglib.eg.org/handle/10.2312/eurovisshort.20141163.091-095
}
}