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

University of Delaware
University of Kentucky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	150-154
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755603
Publication status	Published - 1 Jun 2019
Externally published	Yes
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: 2 Jun 2019 → 5 Jun 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	2/06/19 → 5/06/19

Keywords

Graph signal processing
blue noise dithering
sampling

Access to Document

10.1109/DSW.2019.8755603

Cite this

@inproceedings{e1a580db6cf54ed09a0aad28a391546f,

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",

month = jun,

day = "1",

doi = "10.1109/DSW.2019.8755603",

language = "English",

series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "150--154",

booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

}

Parada-Mayorga, A, Lau, DL, Giraldo, JH & Arce, GR 2019, Sampling of Graph Signals with Blue Noise Dithering. in 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings., 8755603, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 150-154, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 2/06/19. https://doi.org/10.1109/DSW.2019.8755603

Sampling of Graph Signals with Blue Noise Dithering. / Parada-Mayorga, Alejandro; Lau, Daniel L.; Giraldo, Jhony H. et al.
2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 150-154 8755603 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sampling of Graph Signals with Blue Noise Dithering

AU - Parada-Mayorga, Alejandro

AU - Lau, Daniel L.

AU - Giraldo, Jhony H.

AU - Arce, Gonzalo R.

PY - 2019/6/1

Y1 - 2019/6/1

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

UR - https://www.scopus.com/pages/publications/85069486308

U2 - 10.1109/DSW.2019.8755603

DO - 10.1109/DSW.2019.8755603

M3 - Conference contribution

AN - SCOPUS:85069486308

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

SP - 150

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

PB - Institute of Electrical and Electronics Engineers Inc.

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

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Keywords

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