Analysis of Uncertain Scalar Data with Hixels

Joshua A. Levine, David Thompson, Janine C. Bennett, Peer-Timo Bremer, Attila Gyulassy, Valerio Pascucci, Philippe Pébay
Min Chen, Hans Hagen, Charles Hansen, Chris Johnson, Arie Kaufman, editors. Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization
September, 2014

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'.

[DOI/EE link]

@incollection{LTBBGPP14,
  author    = {Joshua A. Levine and David Thompson and Janine C. Bennett and Peer-Timo Bremer and Attila Gyulassy and Valerio Pascucci and Philippe P\'{e}bay},
  booktitle = {Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization},
  day       = {19},
  editor    = {Min Chen and Hans Hagen and Charles Hansen and Chris Johnson and Arie Kaufman},
  ee        = {http://dx.doi.org/10.1007/978-1-4471-6497-5_3},
  month     = {9},
  publisher = {Springer},
  series    = {Mathematics + Visualization},
  title     = {Analysis of Uncertain Scalar Data with Hixels},
  year      = {2014}
}