Perceptually Driven Visibility Optimization for Categorical Data Visualization

Abstract

Visualization techniques often use color to present categorical differences to a user. When selecting a color palette, the perceptual qualities of color need careful consideration. Large coherent groups visually suppress smaller groups and are often visually dominant in images. This paper introduces the concept of class visibility used to quantitatively measure the utility of a color palette to present coherent categorical structure to the user. We present a color optimization algorithm based on our class visibility metric to make categorical differences clearly visible to the user. We performed two user experiments on user preference and visual search to validate our visibility measure over a range of color palettes. The results indicate that visibility is a robust measure, and our color optimization can increase the effectiveness of categorical data visualizations.

Categorical Colormap Optimization with Visualization Case Studies

“…Their work is based on CIE L*a*b* space, for which the authors pointed out its limitation, suggested a remedy of using CIE1994 and CIEDE2000, and reasoned about the algorithmic difficulty in using CIEDE2000. Our work differs from [29] in several ways: (i) We focused on the optimization of a color palette rather an individual visualization; (ii) We enabled users to define constraints in a more meaningful way and demonstrated the use of such constraints through a real-world application; (iii) We made use of CIEDE2000, studied a number of optimization algorithms and identified their relative merits in addressing the algorithmic difficulty; and (iv) We provided a web application to make this technique available to a wider range of users.…”

“…Lee et al [29] presented a color optimization algorithm that maximizes the perceptual distances among a set of categorical colors. Their work is based on CIE L*a*b* space, for which the authors pointed out its limitation, suggested a remedy of using CIE1994 and CIEDE2000, and reasoned about the algorithmic difficulty in using CIEDE2000.…”

“…Global optimization techniques have been used previously in the field of visualization (e.g., [36,29]). Fig.…”

“…Lee et al [29] provided an optimization method based on the visual saliency of each point against its surroundings in a visualization. While this is a useful solution for optimizing a specific visualization, it is not suitable for a system to generate numerous visualizations for different datasets using a consistent colormap.…”

“…We thus decided to use CIEDE2000 (cf. CIE76 in [29]), and focus our effort on the algorithmic issues in optimization.…”