Chameleon: Automated Color Palette Adaptation for Dark Mode Data Visualizations

TL;DR

Chameleon is an automated algorithm that transforms light mode visualizations into dark mode, preserving visual clarity and color semantics to improve user experience across mobile platforms.

Contribution

It introduces a novel automated method for adapting visualizations to dark mode, addressing limitations of manual adjustment and color inversion techniques.

Findings

Effective in maintaining color semantics and visual clarity in dark mode

Validated through case studies, expert interviews, and user studies

Outperforms manual and naive inversion methods

Abstract

Dark mode has gained widespread adoption across mobile platforms due to its benefits in reducing eye strain and conserving battery life. However, while the mobile system switches to dark mode, most visualizations remain designed for light mode, causing visual disruptions. Existing methods, such as manual adjustment or color inversion, are either time-consuming or fail to preserve the semantic meaning of colors in visualizations, making them less effective in dark mode. To address this challenge, we propose Chameleon, an algorithm that automatically transforms light mode visualizations into dark mode while maintaining visual clarity and color semantics. By optimizing for luminance contrast, color consistency, and adjacent color differences, Chameleon ensures that the transformed visualizations are legible and visually coherent. Our evaluation includes case study, expert interview, system evaluation, and a user study, and these demonstrate that Chameleon is effective at translating visualizations for dark mode.