Sampling of Graph Signals with Blue Noise Dithering

Alejandro Parada-Mayorga, Daniel L. Lau, Jhony H. Giraldo, Gonzalo R. Arce

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper discusses the generalization of the concept of blue noise sampling from traditional halftoning to signal processing on graphs. Making use of the spatial properties of blue noise, we generate sampling patterns that provide reconstruction errors that are similar to the ones obtained with state of the art approaches. This sampling scheme presents an alternative to those techniques that require spectral decompositions.

Original languageEnglish
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Number of pages5
ISBN (Electronic)9781728107080
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings


Conference2019 IEEE Data Science Workshop, DSW 2019
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation, grant NSF #1815992, by the UDRF foundation strategic initiative award, and by a University of Delaware Dissertation Fellowship Award.

Publisher Copyright:
© 2019 IEEE.


  • Graph signal processing
  • blue noise dithering
  • sampling

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality


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