Low-discrepancy blue noise sampling

Abdalla G.M. Ahmed, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Jianwei Guo, Dong Ming Yan, Hui Huang, Oliver Deussen

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


We present a novel technique that produces two-dimensional low-discrepancy (LD) blue noise point sets for sampling. Using one-dimensional binary van der Corput sequences, we construct two-dimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.

Original languageEnglish (US)
Article number247
JournalACM transactions on graphics
Issue number6
StatePublished - Nov 2016


  • Blue Noise
  • Low Discrepancy
  • Monte Carlo
  • Quasi-Monte Carlo
  • Sampling

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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