Publication:
Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Simple item page
dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Procesado Multimediaes
dc.contributor.authorMartínez Enríquez, Eduardo
dc.contributor.authorCid Sueiro, Jesús
dc.contributor.authorDíaz de María, Fernando
dc.contributor.authorOrtega Gómez, Román Antonio
dc.date.accessioned2020-08-31T10:31:42Z
dc.date.available2020-08-31T10:31:42Z
dc.date.issued2018-02-05
dc.description.abstractTransformations on graphs can provide compact representations of signals with many applications in denoising, feature extraction or compression. In particular, lifting transforms have the advantage of being critically sampled and invertible by construction, but the efficiency of the transform depends on the choice of a good bipartition of the graph into update (U) and prediction (P) nodes. This is the update/prediction (U=P) assignment problem, which is the focus of this paper. We analyze this problem theoretically and derive an optimal U=P assignment under assumptions about signal model and filters. Furthermore, we prove that the best U=P partition is related to the correlation between nodes on the graph and is not the one that minimizes the number of conflicts (connections between nodes of same label) or maximizes the weight of the cut. We also provide experimental results in randomly generated graph signals and real data from image and video signals that validate our theoretical conclusions, demonstrating improved performance over state of the art solutions for this problem.en
dc.description.sponsorshipThis work was supported in part by NSF under Grant CCF-1018977 and in part by the Spanish Ministry of Economy and Competitiveness under Grants TEC2014-53390-P, TEC2014-52289-R, TEC2016-81900-REDT/AEI and TEC2017-83838-Ren
dc.description.statusPublicadoes
dc.format.extent11
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationOptimized Update/Prediction Assignment for Lifting Transforms on Graphs. IEEE Transactions on Signal Processing, (2018), 66(8), pp.: 2098-2111.en
dc.identifier.doihttps://doi.org/10.1109/TSP.2018.2802465
dc.identifier.issn1053-587X
dc.identifier.publicationfirstpage2098
dc.identifier.publicationissue8
dc.identifier.publicationlastpage2111
dc.identifier.publicationtitleIEEE Transactions on Signal Processingen
dc.identifier.publicationvolume66
dc.identifier.urihttps://hdl.handle.net/10016/26244
dc.identifier.uxxiAR/0000020813
dc.language.isoeng
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TEC2014-53390-Pes
dc.relation.projectIDGobierno de España. TEC2014-52289-Res
dc.relation.projectIDGobierno de España. TEC2016-81900-REDT/AEIes
dc.relation.projectIDGobierno de España. TEC2017-83838-Res
dc.rights© 2018 IEEEen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherLifting transformen
dc.subject.otherGraphsen
dc.subject.otherU/P Assignmenten
dc.subject.otherSplittingen
dc.subject.otherGraph bipartitionen
dc.titleOptimized Update/Prediction Assignment for Lifting Transforms on Graphsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
optimized_TSP_2018.pdf
Size:
1.19 MB
Format:
Adobe Portable Document Format
Download
Collections
DTSC - GPM - Artículos de Revistas