Colorgorical: Creating discriminable and preferable color palettes for information visualization

We present an evaluation of Colorgorical, a web-based tool for creating discriminable and aesthetically preferable categorical

color palettes. Colorgorical uses iterative semi-random sampling to pick colors from CIELAB space based on user-defined

discriminability and preference importances. Colors are selected by assigning each a weighted sum score that applies the userdefined

importances to Perceptual Distance, Name Difference, Name Uniqueness, and Pair Preference scoring functions, which

compare a potential sample to already-picked palette colors. After, a color is added to the palette by randomly sampling from the

highest scoring palettes. Users can also specify hue ranges or build off their own starting palettes. This procedure differs from

previous approaches that do not allow customization (e.g., pre-made ColorBrewer palettes) or do not consider visualization design

constraints (e.g., Adobe Color and ACE). In a Palette Score Evaluation, we verified that each scoring function measured different color

information. Experiment 1 demonstrated that slider manipulation generates palettes that are consistent with the expected balance

of discriminability and aesthetic preference for 3-, 5-, and 8-color palettes, and also shows that the number of colors may change

the effectiveness of pair-based discriminability and preference scores. For instance, if the Pair Preference slider were upweighted,

users would judge the palettes as more preferable on average. Experiment 2 compared Colorgorical palettes to benchmark palettes

(ColorBrewer, Microsoft, Tableau, Random). Colorgorical palettes are as discriminable and are at least as preferable or more preferable

than the alternative palette sets. In sum, Colorgorical allows users to make customized color palettes that are, on average, as

effective as current industry standards by balancing the importance of discriminability and aesthetic preference.

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