Subjective quantification of perceptual interactions among some 2D scientific visualization methods

Daniel Acevedo*, David H. Laidlaw

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

We present an evaluation of a parameterized set of 2D icon-based visualization methods where we quantified how perceptual interactions among visual elements affect effective data exploration. During the experiment, subjects quantified three different design factors for each method: the spatial resolution it could represent, the number of data values it could display at each point, and the degree to which it is visually linear. The class of visualization methods includes Poisson-disk distributed icons where icon size, icon spacing, and icon brightness can be set to a constant or coupled to data values from a 2D scalar field. By only coupling one of those visual components to data, we measured filtering interference for all three design factors. Filtering interference characterizes how different levels of the constant visual elements affect the evaluation of the data-coupled element. Our novel experimental methodology allowed us to generalize this perceptual information, gathered using ad-hoc artificial datasets, onto quantitative rules for visualizing real scientific datasets. This work also provides a framework for evaluating visualizations of multi-valued data that incorporate additional visual cues, such as icon orientation or color.

Original languageEnglish (US)
Pages (from-to)1133-1140
Number of pages8
JournalIEEE Transactions on Visualization and Computer Graphics
Volume12
Issue number5
DOIs
StatePublished - Sep 1 2006

Keywords

  • 2D visualization methods
  • Perception models
  • Perceptual interactions
  • Visual design
  • Visualization evaluation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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