Lu Kecheng, Feng Mi, Chen Xin, Sedlmair Michael, Deussen Oliver, Lischinski Dani, Cheng Zhanglin, Wang Yunhai
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):475-484. doi: 10.1109/TVCG.2020.3030406. Epub 2021 Jan 28.
We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.
我们提出了一种综合方法，用于为多类散点图、折线图和柱状图等不同可视化创建调色板并进行分配。虽然其他方法将颜色的创建与分配分开，但我们的方法会考虑数据特征来生成调色板，然后以促进更好地对类别进行视觉区分的方式进行分配。为此，我们使用基于模拟退火的定制优化来最大化三个精心设计的颜色评分函数的组合：点的清晰度、名称差异和颜色辨别力。我们通过针对散点图和折线图的对照用户研究，将我们的方法与最先进的调色板进行比较，此外还进行了案例研究。我们的结果表明，作为一种完全自动化的方法，Palettailor生成的调色板比现有方法具有更高的辨别质量。我们优化的效率还使我们能够将用户修改纳入颜色选择过程。
