Year: 2025
Type: preprint
Abstract: We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images. We then examined how these scores align with 12 image-based metrics spanning information-theoretic, clutter, color, and our two object-based metrics. Our results show that both low-level image properties and the high-level elements affect perceived VC in visualization images; The number of corners and distinct colors are robust metrics across visualizations. Second, feature congestion, an information-theoretic metric capturing statistical patterns in color and texture, is the strongest predictor of perceived complexity in visualizations rich in the same stimuli; edge density effectively explains VC in node-link diagrams. Additionally, we observe a bell-curve effect for text annotations: increasing text-to-ink ratio (TiR) initially reduces complexity, reaching an optimal point, beyond which further text increases perceived complexity. Our quantification pipeline is also interpretable, enabling metric-based explanations, grounded in the VisComplexity2K dataset, bridging computational metrics with human perceptual responses. osf.io/5xe8a has the preregistration and osf.io/bdet6 has the VisComplexity2K dataset, source code, and all Apdx. and figures.
Source: arXiv (Cornell University)
Authors: Chu, Mengdi, Qiu, Zefeng, Ling, Meng, Jiang, Shuning, Laramee, Robert S., Sedlmair, Michael, Chen, Jian
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7087863040
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