Scientific Visualization

Part of the series Mathematics and Visualization pp 35-44


Analysis of Uncertain Scalar Data with Hixels

  • Joshua A. LevineAffiliated withSchool of Computing, Clemson Univeristy Email author 
  • , David ThompsonAffiliated withKitware, Inc.
  • , Janine C. BennettAffiliated withSandia National Laboratories
  • , Peer-Timo BremerAffiliated withLawrence Livermore National Laboratory
  • , Attila GyulassyAffiliated withScientific Computing and Imaging Institute, University of Utah
  • , Valerio PascucciAffiliated withScientific Computing and Imaging Institute, University of Utah
  • , Philippe P. PébayAffiliated withSandia National Laboratories

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One of the greatest challenges for today’s visualization and analysis communities is the massive amounts of data generated from state of the art simulations. Traditionally, the increase in spatial resolution has driven most of the data explosion, but more recently ensembles of simulations with multiple results per data point and stochastic simulations storing individual probability distributions are increasingly common. This chapter describes a relatively new data representation for scalar data, called hixels, that stores a histogram of values for each sample point of a domain. The histograms may be created by spatial down-sampling, binning ensemble values, or polling values from a given distribution. In this manner, hixels form a compact yet information rich approximation of large scale data. In essence, hixels trade off data size and complexity for scalar-value “uncertainty”.