Image-Based Aspect Ratio Selection
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AbstractSelecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.
All Author(s) ListYunhai Wang, Zeyu Wang, Chi-Wing Fu, Hansjorg Schmauder, Oliver Deussen, Daniel Weiskopf
Journal nameIEEE Transactions on Visualization and Computer Graphics
Detailed descriptionfull paper in IEEE Visualization week 2018 (InfoVis track)
Volume Number25
Issue Number1
Pages840 - 849
LanguagesEnglish-United States

Last updated on 2020-21-01 at 01:32