Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

Color is one of the most effective visual variables since it can be combined with other mappings and encode information without using any additional space on the display. An important example where expressing additional visual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity is desirable in this application, because the user intuitively perceives clusters and relations among multi-dimensional data points. Many approaches use two-dimensional colormaps in their analysis, which are typically created by interpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colors are either saturated and discriminative but not perceptual linear or vice versa. A solution that combines both advantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in Information Visualization according to our literature analysis. The method maps high-dimensional data points into the CIELAB color 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 analysis of high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.