Sampling of Graph Signals with Blue Noise Dithering

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

doi:10.1109/DSW.2019.8755603

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 proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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 language	English
Title of host publication	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Pages	150-154
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755603
State	Published - Jun 2019
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: Jun 2 2019 → Jun 5 2019

Publication series

Name	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
Country/Territory	United States
City	Minneapolis
Period	6/2/19 → 6/5/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

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

Access to Document

10.1109/DSW.2019.8755603

Cite this

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title = "Sampling of Graph Signals with Blue Noise Dithering",

abstract = "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.",

keywords = "Graph signal processing, blue noise dithering, sampling",

author = "Alejandro Parada-Mayorga and Lau, {Daniel L.} and Giraldo, {Jhony H.} and Arce, {Gonzalo R.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",

year = "2019",

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doi = "10.1109/DSW.2019.8755603",

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AU - Lau, Daniel L.

AU - Giraldo, Jhony H.

AU - Arce, Gonzalo R.

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Graph signal processing

KW - blue noise dithering

KW - sampling

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M3 - Conference contribution

AN - SCOPUS:85069486308

T3 - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

SP - 150

EP - 154

BT - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

T2 - 2019 IEEE Data Science Workshop, DSW 2019

Y2 - 2 June 2019 through 5 June 2019

ER -

Sampling of Graph Signals with Blue Noise Dithering

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this