Year: 2024
Type: article
Abstract: 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.
Authors: Kecheng Lu, Lihang Zhu, Yunhai Wang, Qiong Zeng, Weitao Song, Khairi Reda
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Natural Science Foundation of China, Renmin University of China, Natural Science Foundation of Beijing Municipality, Natural Science Foundation of Shandong Province, Universität Stuttgart, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
OpenAlex ID: W4405908072