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The scalability of a particular visualization approach is limited by theability for people to discern differences between plots made with differentdatasets. Ideally, when the data changes, the visualization changes inperceptible ways. This relation breaks down when there is a mismatch betweenthe encoding and the character of the dataset being viewed. Unfortunately,visualizations are often designed and evaluated without fully exploring howthey will respond to a wide variety of datasets. We explore the use of an imagesimilarity measure, the Multi-Scale Structural Similarity Index (MS-SSIM), fortesting the discriminability of a data visualization across a variety ofdatasets. MS-SSIM is able to capture the similarity of two visualizationsacross multiple scales, including low level granular changes and high levelpatterns. Significant data changes that are not captured by the MS-SSIMindicate visualizations of low discriminability and effectiveness. Themeasure's utility is demonstrated with two empirical studies. In the first, wecompare human similarity judgments and MS-SSIM scores for a collection ofscatterplots. In the second, we compute the discriminability values for a setof basic visualizations and compare them with empirical measurements ofeffectiveness. In both cases, the analyses show that the computational measureis able to approximate empirical results. Our approach can be used to rankcompeting encodings on their discriminability and to aid in selectingvisualizations for a particular type of data distribution.

Discriminability Tests for Visualization Effectiveness and Scalability