Visual Coherence for Large-Scale Line-Plot Visualizations

Philipp Muigg, Markus Hadwiger, Helmut Doleisch, Meister Eduard Gröller
Visual Coherence for Large-Scale Line-Plot Visualizations
Computer Graphics Forum, 30(3):643-652, June 2011. [Paper]

Information

Abstract

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 2x2 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.

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BibTeX

@article{Muigg_2011_VC,
  title =      "Visual Coherence for Large-Scale Line-Plot Visualizations",
  author =     "Philipp Muigg and Markus Hadwiger and Helmut Doleisch and
               Meister Eduard Gr{"o}ller",
  year =       "2011",
  abstract =   "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 2x2 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.",
  month =      jun,
  issn =       "0167-7055",
  journal =    "Computer Graphics Forum",
  number =     "3",
  volume =     "30",
  booktitle =  "Computer Graphics Forum",
  organization = "The Eurographics Association and Blackwell Publishing Ltd.",
  publisher =  "Blackwell Publishing",
  pages =      "643--652",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/Muigg_2011_VC/",
}