# What Makes a Visualization Image Complex?

arXiv: https://arxiv.org/abs/2510.08332 · HTML: https://mlsanity.com/paper/2510.08332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2025.

## Built on by (most cited first, 2 of 2)

- [Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection](https://mlsanity.com/paper/2604.26174/) (2026)
- [Towards benchmarking Western Bluebird detection in the wild](https://mlsanity.com/paper/2610.07802/) (2026)

## Abstract (arXiv)

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.