Computing the discrepancy with applications to supersampling patterns

  • Authors:
  • David P. Dobkin;David Eppstein;Don P. Mitchell

  • Affiliations:
  • Princeton Univ., Princeton, NJ;Univ. of California, Irvine;Princeton Univ., Princeton, NJ

  • Venue:
  • ACM Transactions on Graphics (TOG)
  • Year:
  • 1996

Quantified Score

Hi-index 0.00

Visualization

Abstract

Patterns used for supersampling in graphics have been analyzed from statistical and signal-processing viewpoints. We present an analysis based on a type of isotropic discrepancy—how good patterns are at estimating the area in a region of defined type. We present algorithms for computing discrepancy relative to regions that are defined by rectangles, halfplanes, and higher-dimensional figures. Experimental evidence shows that popular supersampling patterns have discrepancies with better asymptotic behavior than random sampling, which is not inconsistent with theoretical bounds on discrepancy.