Navigating Color Constraints in Multi-View Visualizations with MVcolor

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.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1109/pacificvis64226.2025.00020
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00

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article

Navigating Color Constraints in Multi-View Visualizations with MVcolor

Zijian Tian, Yi‐Chieh Lee, Yun-Rou Lin, Fu‐Yin Cherng et al.
Data Visualization and Analytics
article

Navigating Color Constraints in Multi-View Visualizations with MVcolor

Zijian Tian, Yi‐Chieh Lee, Yun-Rou Lin, Fu‐Yin Cherng, Wen–Chieh Lin
article en

Abstract

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.

National Chung Cheng University (TW), National Yang Ming Chiao Tung University (TW), National University of Singapore (SG)
National Science and Technology Council
Openalex Percentile: Top 88%
Data Visualization and Analytics
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