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

Kecheng Lu¹ Lihang Zhu² Yunhai Wang¹ Qiong Zeng² Weitao Song³ Khairi Reda⁴

¹Renmin University of China ²Shandong University ³Beijing Institute of Technology ⁴Indiana University Indianapolis

Accepted by IEEE Transactions on Visualization and Computer Graphics

Figure 1: Results of applying our approach (right-most) to optimize three overlapping histograms. (a) shows the input component distributions; (b, c, d) illustrates the results of applying three comparable benchmarks: standard alpha blending, local color blending, and hue-preserving color blending using base colors from the Tableau-10 palette and a uniform opacity of $\alpha=0.5$. By comparison, our approach (e), which embodies a color-name aware optimization, auto-generates optimal color, transparency and rendering order settings, ensuring discriminability for all segments while improving whole-from-parts perception. Our technique can also optimize other overlapped visualizations, including Venn diagrams (f vs. g).

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.

Online Website: Github

Figures:

Figure 2: Preprocessing of the objective function. The input distributions are shown in (a). Rendering these three histograms (b) results in six regions R_1, ..., R_6, with some regions representing an intersection of two or more histograms. The binary membership matrix (c) lists the membership for each region, with M_{i,j} indicating whether region i belongs to histogram (i.e., class) j. For example, the region $R_4$ belongs to the 1st class and 2nd class, which are classes A and B. The neighborhood graph (d) indicates region adjacency, with node size corresponding to the size of the region.

Figure 3: The influence of different weight settings ($\omega_1$, $\omega_2$, $\omega_3$) on the final colorization results, showing the contribution of each of the three objective-function terms individually. (a) illustrates the effects of optimizing within-class association only (1, 0, 0). (b) shows between-class discrimination only (0, 1, 0). (c) illustrates the effect of maximizing color separability (0, 0, 1). (d) illustrates the final result with all three terms equally weighted (1, 1, 1). The legend also depicts the optimized rendering order (from top to bottom). The input distributions are the same as those shown in Figure 2-a.

Figure 5: Illustration for the three tasks used in our experiments: (a) distribution estimation, (b) class discrimination, and (c) user preference. The correct answer for the distribution estimation task is option B, while the correct number of classes in the discrimination task is three classes.

Figure 14: Visualizing the Scan Bio dataset with illustrative parallel coordinates. results generated by using (a) default opacity weightings and standard alpha blending, (b) local color blending, and (c) our optimization.

Figure 15: An elliptical Venn diagram shown with (a) ColorBrewer palette and uniform opacity (0.5) using local blending, and (b) hue-preserving blending. Both visualizations lead to suboptimal compositing. By contrast, our optimization (c) produces a more perceptible set visualization which guarantees minimum color separability for all parts. (d) Shows the same colors as (c) but with uniform, non-optimized opacities.

Materials:


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Acknowledgements:

This work is supported by the grants of the National Key R&D Program of China under Grant 2022ZD0160805, NSFC (No.62132017), the Shandong Provincial Natural Science Foundation (No.ZQ2022JQ32), the Fundamental Research Funds for the Central Universities, the Beijing Natural Science Foundation (L247027), and the Research Funds of Renmin University of China. The authors would like to thank Daniel Weiskopf (University of Stuttgart) for his valuable insights and guidance.