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Colorgorical: Creating discriminable and preferable color palettes for information visualization

机译:Colorgorical:为信息可视化创建可分辨的首选调色板

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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 user-defined 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.
机译:我们对Colorgorical进行了评估,Colorgorical是一个基于网络的工具,用于创建可区分且在美学上更可取的分类调色板。 Colorgorical使用迭代半随机采样根据用户定义的可分辨性和偏好重要性从CIELAB空间中选择颜色。通过为每个颜色分配加权总和得分,将用户定义的重要性应用于“感知距离”,“名称差异”,“名称唯一性”和“配对首选项”评分功能,从而将颜色样本与已经挑选的调色板颜色进行比较,从而选择颜色。之后,通过从得分最高的调色板中随机采样,将颜色添加到调色板中。用户还可以指定色相范围或构建自己的起始调色板。此过程与不允许自定义(例如,预制的ColorBrewer调色板)或不考虑可视化设计约束(例如,Adobe Color和ACE)的以前的方法不同。在调色板评分评估中,我们验证了每个评分功能都测量了不同的颜色信息。实验1证明,滑块操作生成的调色板与3、5和8色调色板的可辨别性和美学偏爱的预期平衡相一致,并且还表明颜色的数量可能会更改基于对的可辨别性的有效性和偏好分数。例如,如果增加了“配对首选项”滑块,则用户将平均判断调色板为最佳。实验2将Colorgorical调色板与基准调色板(ColorBrewer,Microsoft,Tableau,Random)进行了比较。同色调色板是可区分的,并且至少比替代调色板集更好或更优选。总而言之,Colorgorical允许用户通过平衡可区分性和审美偏好的重要性来制作平均水平与当前行业标准一样有效的定制调色板。

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