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What Makes a Visualization Image Complex?

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    Researchers quantified perceived visual complexity in data visualizations using objective metrics. They found that elements like corners, distinct colors, and text-to-ink ratio significantly impact how complex visualizations appear.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Perceptual Science

    Background:

    • Perceived visual complexity (VC) is crucial for effective data visualization.
    • Objective metrics are needed to quantify VC and understand user perception.
    • Existing metrics may not fully capture the nuances of VC in diverse visualizations.

    Purpose of the Study:

    • To investigate perceived visual complexity in data visualizations using objective image-based metrics.
    • To evaluate the alignment of human-scored VC with various computational metrics.
    • To develop and validate a quantifiable model for predicting perceived VC.

    Main Methods:

    • Conducted a large-scale crowdsourcing experiment with 349 participants rating 1,800 visualization images for VC.
    • Analyzed 12 image-based metrics, including pixel-based, clutter, color, shape, and novel object-based metrics (meaningful-color-count (MeC) and text-to-ink ratio (TiR)).
    • Developed a quantification model grounded in the VisComplexity2K dataset.

    Main Results:

    • Both low-level features (edges, corners) and high-level elements (distinct colors) significantly influence perceived VC.
    • Feature congestion is a strong predictor for visualizations with continuous color/texture, while edge density is effective for node-link diagrams.
    • A bell-curve relationship was observed for text-to-ink ratio (TiR), indicating an optimal text density for reducing complexity.

    Conclusions:

    • Objective metrics, including novel ones like MeC and TiR, can effectively predict perceived visual complexity in data visualizations.
    • Understanding the interplay of low-level and high-level features provides insights into visualization design.
    • The developed model offers interpretable, metric-based explanations for perceived VC, bridging computational approaches and human perception.