Multiple Descriptions Using Sparse Decompositions

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Abstract

In this paper, we consider the design of multiple descriptions (MDs) using sparse decompositions. In a description erasure channel only a subset of the transmitted descriptions is received. The MD problem concerns the design of the descriptions such that they individually approximate the source and furthermore are able to refine each other. In this paper, we form descriptions using convex optimization with l1-norm minimization and Euclidean distortion constraints on the reconstructions and show that with this method we can obtain non-trivial descriptions. We give an algorithm based on recently developed first-order method to the proposed convex problem such that we can solve large-scale instances for image sequences.
Original languageEnglish
JournalProceedings of the European Signal Processing Conference
Volume2010
Pages (from-to)110-114
ISSN2076-1465
Publication statusPublished - 2010
EventEuropean Signal Processing Conference 2010 - Aalborg, Denmark
Duration: 23 Aug 201027 Aug 2010

Conference

ConferenceEuropean Signal Processing Conference 2010
CountryDenmark
CityAalborg
Period23/08/201027/08/2010

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Decomposition
Convex optimization

Cite this

@inproceedings{1f0ddb6600bc495ca7a4fcc7a0fde6f1,
title = "Multiple Descriptions Using Sparse Decompositions",
abstract = "In this paper, we consider the design of multiple descriptions (MDs) using sparse decompositions. In a description erasure channel only a subset of the transmitted descriptions is received. The MD problem concerns the design of the descriptions such that they individually approximate the source and furthermore are able to refine each other. In this paper, we form descriptions using convex optimization with l1-norm minimization and Euclidean distortion constraints on the reconstructions and show that with this method we can obtain non-trivial descriptions. We give an algorithm based on recently developed first-order method to the proposed convex problem such that we can solve large-scale instances for image sequences.",
author = "Jensen, {Tobias Lindstr{\o}m} and Jan {\O}stergaard and Joachim Dahl and Jensen, {S{\o}ren Holdt}",
year = "2010",
language = "English",
volume = "2010",
pages = "110--114",
journal = "Proceedings of the European Signal Processing Conference",
issn = "2076-1465",
publisher = "European Association for Signal Processing (EURASIP)",

}

Multiple Descriptions Using Sparse Decompositions. / Jensen, Tobias Lindstrøm; Østergaard, Jan; Dahl, Joachim; Jensen, Søren Holdt.

In: Proceedings of the European Signal Processing Conference, Vol. 2010, 2010, p. 110-114.

Research output: Contribution to journalConference article in JournalResearchpeer-review

TY - GEN

T1 - Multiple Descriptions Using Sparse Decompositions

AU - Jensen, Tobias Lindstrøm

AU - Østergaard, Jan

AU - Dahl, Joachim

AU - Jensen, Søren Holdt

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N2 - In this paper, we consider the design of multiple descriptions (MDs) using sparse decompositions. In a description erasure channel only a subset of the transmitted descriptions is received. The MD problem concerns the design of the descriptions such that they individually approximate the source and furthermore are able to refine each other. In this paper, we form descriptions using convex optimization with l1-norm minimization and Euclidean distortion constraints on the reconstructions and show that with this method we can obtain non-trivial descriptions. We give an algorithm based on recently developed first-order method to the proposed convex problem such that we can solve large-scale instances for image sequences.

AB - In this paper, we consider the design of multiple descriptions (MDs) using sparse decompositions. In a description erasure channel only a subset of the transmitted descriptions is received. The MD problem concerns the design of the descriptions such that they individually approximate the source and furthermore are able to refine each other. In this paper, we form descriptions using convex optimization with l1-norm minimization and Euclidean distortion constraints on the reconstructions and show that with this method we can obtain non-trivial descriptions. We give an algorithm based on recently developed first-order method to the proposed convex problem such that we can solve large-scale instances for image sequences.

M3 - Conference article in Journal

VL - 2010

SP - 110

EP - 114

JO - Proceedings of the European Signal Processing Conference

JF - Proceedings of the European Signal Processing Conference

SN - 2076-1465

ER -