Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems.Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution and style transfer.We explore the application of such metrics to judgments of visualization similarity.We extend a similarity metric using five ML architectures and three pre-trained weight sets.We replicate results from previous crowdsourced studies on scatterplot and visual channel similarity perception.Notably, our metric using pre-trained ImageNet weights outperformed gradient-descent tuned MS-SSIM, a multi-scale similarity metric based on luminance, contrast, and structure.Our work contributes to understanding how deep-feature-based metrics can enhance similarity assessments in visualization, potentially improving visual analysis tools and techniques.Supplementary materials are available at https://osf.io/dj2ms/.

Y-axis truncation is a well-known, much-debated visualization practice. Our work complements existing empirical work by providing a systematic analysis of y-axis truncation on grouped bar charts. Drawing upon theoretical frameworks such as Algebraic Visualization Design, we examine how structure-preserving modifications to visualization affect user performance by systematically dividing the space of possible truncations according to their monotonicity and the type of relations in the underlying data. Our results demonstrate that for comparing and estimating the difference between the lengths of two bars, truncating the y-axis does not affect task performance. For comparing or estimating the relative growth between two bars, truncating monotonically has similar performance to no truncation, while truncating non-monotonically is very likely to impair performance. We discuss possible extensions of our work and recommendations for y-axis truncation. All supplementary materials are available at https://osf.io/k4hjd/?view_only=008b087fc3d94be7ba0ce7aea95012a7.