Lu Kecheng, Zhu Lihang, Wang Yunhai, Zeng Qiong, Song Weitao, Reda Khairi
IEEE Trans Vis Comput Graph. 2025 Sep;31(9):6617-6632. doi: 10.1109/TVCG.2024.3520219.
Transparency is commonly utilized in visualizations to overlay color-coded histograms or sets, thereby facilitating the visual comparison of categorical data. However, these charts often suffer from significant overlap between objects, resulting in substantial color interactions. Existing color blending models struggle in these scenarios, frequently leading to ambiguous color mappings and the introduction of false colors. To address these challenges, we propose an automated approach for generating optimal color encodings to enhance the perception of translucent charts. Our method harnesses color nameability to maximize the association between composite colors and their respective class labels. We introduce a color-name aware (CNA) optimization framework that generates maximally coherent color assignments and transparency settings while ensuring perceptual discriminability for all segments in the visualization. We demonstrate the effectiveness of our technique through crowdsourced experiments with composite histograms, showing how our technique can significantly outperform both standard and visualization-specific color blending models. Furthermore, we illustrate how our approach can be generalized to other visualizations, including parallel coordinates and Venn diagrams. We provide an open-source implementation of our technique as a web-based tool.
透明度通常用于可视化中，以叠加颜色编码的直方图或数据集，从而便于对分类数据进行视觉比较。然而，这些图表常常存在对象之间的大量重叠，导致显著的颜色交互。现有的颜色混合模型在这些场景中表现不佳，常常导致模糊的颜色映射并引入伪色。为应对这些挑战，我们提出一种自动方法来生成最优颜色编码，以增强半透明图表的感知效果。我们的方法利用颜色可命名性来最大化合成颜色与其各自类别标签之间的关联。我们引入了一个颜色名称感知（CNA）优化框架，该框架生成最大程度连贯的颜色分配和透明度设置，同时确保可视化中所有部分的感知可区分性。我们通过对合成直方图进行众包实验来证明我们技术的有效性，展示了我们的技术如何显著优于标准颜色混合模型和特定于可视化的颜色混合模型。此外，我们说明了我们的方法如何可以推广到其他可视化，包括平行坐标图和维恩图。我们提供了我们技术的开源实现，作为一个基于网络的工具。
