Navigating Color Constraints in Multi-View Visualizations with MVcolor

• Published in: IEEE Pacific Visualization Symposium pp. 138 - 148
• Main Authors: Lin, Yun-Rou, Cherng, Fu-Yin, Lee, Yi-Chieh, Tian, Zhu-Ying, Lin, Wen-Chieh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.04.2025
• Subjects: Data visualization; Encoding; Human computer interaction; Human-centered computing; Image color analysis; Memory management; Navigation; Recommender systems; Semantics; User experience; Visualization; Visualization design and evaluation methods
• ISSN: 2165-8773
• DOI: 10.1109/PacificVis64226.2025.00020

Multi-view visualizations have gained prominence for their ability to simultaneously present multiple perspectives in a single display, aiding users in making informed decisions. Although several tools have been developed to facilitate the design of multi-view visualizations, there is a lack of support for effective color design in these systems. Thoughtful color design can enhance readability and provide cues, while careless color choices may lead to confusion. Moreover, human short-term memory imposes constraints on the number of colors that can be effectively employed in a multi-view visualization. To address these challenges, we introduce MVcolor, a color encoding recommendation system designed to maintain color encoding consistency and ensure adequate color discriminability within the constraints of human short-term memory. Our approach employs a unified color scheme across the entire multi-view visualization and groups views and visual objects based on their visual and semantic similarities. We conducted user studies to evaluate the effectiveness of our system and demonstrate its ability to improve color encoding in multi-view visualizations.