JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (6): 51-63.doi: 10.6040/j.issn.1671-9352.0.2026.004

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Revisiting color differences modeling for complex visualizations

HUANG Yilin1,2, HU Zuorun1, WANG Yunhai3, LIU Shu2, ZENG Qiong1*   

  1. 1. School of Computer Science and Technology &
    Shandong Provincial Key Laboratory of Computing-Network Integration, Shandong University, Qingdao 266237, Shandong, China;
    2. School of Computer Science and Engineering, Central South University, Changsha 410083, Hunan, China;
    3. School of Information Resource Management, Renmin University of China, Beijing 100872, China
  • Published:2026-06-04

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Abstract: Color, as an important visual encoding channel, is often used in conjunction with visual marks such as points, lines, and areas to represent abstract data. Color difference has great impact on the discriminability and readability of visualizations. Previous research has shown that color difference perception is influenced by the shape and size of graphical elements, yet the extent of this influence in relation to interfering markings remains under-explored. To address this, this paper explored the perception of color differences among various graphical elements within complex visualization environments containing interference colors. We generated data with different interference colors and visual elements(such as scatterplots, bar charts, and line charts), designed and implemented an experimental system to validate color difference perception in complex visualization contexts, conducted a crowdsourcing experiment on Prolific, and performed a multi-faceted analysis of the experimental results to construct color difference models among different visualization settings. Our experimental results demonstrated that interference colors had a significant impact on users perception of color differences. Moreover, the perception of color differences between different interference colors was closely related to the shape and size of the graphical elements.

Key words: color difference perception, color difference modeling, crowdsourcing experiments, data visualization

CLC Number: 

  • TP391
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