Color-Name Aware Optimization to Enhance the Perception of Transparent Overlapped Charts

TL;DR

This paper presents a color optimization method that improves the perception of overlapping transparent charts by maximizing color distinguishability and label association, addressing issues with color blending in overlapped visualizations.

Contribution

We introduce a color-name aware optimization framework that enhances transparent chart perception by generating coherent color and transparency settings, outperforming existing blending models.

Findings

Significant improvement in color distinguishability in overlapped charts.

Crowdsourced experiments validate the effectiveness of the proposed method.

Applicable to various visualization types like histograms, parallel coordinates, and Venn diagrams.

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.