Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

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dc.contributor.author Martínez Enríquez, Eduardo
dc.contributor.author Cid Sueiro, Jesús
dc.contributor.author Díaz de María, Fernando
dc.contributor.author Ortega, Antonio
dc.date.accessioned 2018-02-13T11:59:49Z
dc.date.available 2018-02-13T11:59:49Z
dc.date.issued 2018-02-05
dc.identifier.bibliographicCitation Optimized Update/Prediction Assignment for Lifting Transforms on Graphs. IEEE Transactions on Signal Processing, vol.PP (issue 99)
dc.identifier.issn 1053-587X
dc.identifier.uri http://hdl.handle.net/10016/26244
dc.description.abstract Transformations 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.
dc.description.sponsorship This 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-R
dc.format.extent 15
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.subject.other Lifting transform
dc.subject.other Graphs
dc.subject.other U/P Assignment
dc.subject.other Splitting
dc.subject.other Graph bipartition
dc.title Optimized Update/Prediction Assignment for Lifting Transforms on Graphs
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TSP.2018.2802465
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2014-53390-P
dc.relation.projectID Gobierno de España. TEC2014-52289-R
dc.relation.projectID Gobierno de España. TEC2016-81900-REDT/AEI
dc.relation.projectID Gobierno de España. TEC2017-83838-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 14
dc.identifier.publicationtitle IEEE Transactions on Signal Processing
dc.identifier.uxxi AR/0000020813
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