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

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	150-154
Nombre de pages	5
ISBN (Electronique)	9781728107080
Les DOIs	https://doi.org/10.1109/DSW.2019.8755603
état	Publié - 1 juin 2019
Modification externe	Oui
Evénement	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, États-Unis Durée: 2 juin 2019 → 5 juin 2019

Série de publications

Nom	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Une conférence

Une conférence	2019 IEEE Data Science Workshop, DSW 2019
Pays/Territoire	États-Unis
La ville	Minneapolis
période	2/06/19 → 5/06/19

Accès au document

10.1109/DSW.2019.8755603

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@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. Dans 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings., 8755603, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., p. 150-154, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, États-Unis, 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).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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

EP - 154

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

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Contient cette citation