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

Producción científica: Conference contribution › revisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Páginas	150-154
Número de páginas	5
ISBN (versión digital)	9781728107080
DOI	https://doi.org/10.1109/DSW.2019.8755603
Estado	Published - jun 2019
Evento	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duración: jun 2 2019 → jun 5 2019

Serie de la publicación

Nombre	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
País/Territorio	United States
Ciudad	Minneapolis
Período	6/2/19 → 6/5/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

Financiación

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.

Financiadores	Número del financiador
National Science Foundation Arctic Social Science Program	1815992, 1816003
National Science Foundation Arctic Social Science Program
University of Delaware Research Foundation
Delaware State University

ASJC Scopus subject areas

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

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10.1109/DSW.2019.8755603

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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",

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AU - Lau, Daniel L.

AU - Giraldo, Jhony H.

AU - Arce, Gonzalo R.

PY - 2019/6

Y1 - 2019/6

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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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DO - 10.1109/DSW.2019.8755603

M3 - Conference contribution

AN - SCOPUS:85069486308

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

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ER -

Sampling of Graph Signals with Blue Noise Dithering

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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