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Visual Coherence for Large‐Scale Line‐Plot Visualizations
Author(s) -
Muigg Philipp,
Hadwiger Markus,
Doleisch Helmut,
Gröller Eduard
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01913.x
Subject(s) - computer science , parallel coordinates , visualization , rendering (computer graphics) , data visualization , artificial intelligence , computer graphics (images) , computer vision
Displaying a large number of lines within a limited amount of screen space is a task that is common to many different classes of visualization techniques such as time‐series visualizations, parallel coordinates, link‐node diagrams, and phase‐space diagrams. This paper addresses the challenging problems of cluttering and overdraw inherent to such visualizations. We generate a 2×2 tensor field during line rasterization that encodes the distribution of line orientations through each image pixel. Anisotropic diffusion of a noise texture is then used to generate a dense, coherent visualization of line orientation. In order to represent features of different scales, we employ a multi‐resolution representation of the tensor field. The resulting technique can easily be applied to a wide variety of line‐based visualizations. We demonstrate this for parallel coordinates, a time‐series visualization, and a phase‐space diagram. Furthermore, we demonstrate how to integrate a focus+context approach by incorporating a second tensor field. Our approach achieves interactive rendering performance for large data sets containing millions of data items, due to its image‐based nature and ease of implementation on GPUs. Simulation results from computational fluid dynamics are used to evaluate the performance and usefulness of the proposed method.