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Analysis of tensor approximation for compression-domain volume visualization


Ballester-Ripoll, Rafael; Suter, Susanne K; Pajarola, Renato (2015). Analysis of tensor approximation for compression-domain volume visualization. Computers & Graphics, 47:34-47.

Abstract

As modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higher-order tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Additionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of compression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations.

Abstract

As modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higher-order tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Additionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of compression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > General Engineering
Physical Sciences > Human-Computer Interaction
Physical Sciences > Computer Graphics and Computer-Aided Design
Uncontrolled Keywords:visualization, tensor approximation, multiresolution, volume rendering, level-of-detail
Language:English
Date:April 2015
Deposited On:30 Oct 2015 07:27
Last Modified:26 Jan 2022 06:52
Publisher:Elsevier
ISSN:0097-8493
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.cag.2014.10.002
Other Identification Number:merlin-id:10254
Project Information:
  • : FunderSNSF
  • : Grant ID200021_132521
  • : Project TitleInteractive Multiscale Visualization and Analysis of Very Large Structural Volume Data
  • : FunderSNSF
  • : Grant IDPBZHP2_147309
  • : Project TitleSparse Data Compression for GPU-based Direct Volume Visualization
  • : FunderFP7
  • : Grant ID290227
  • : Project TitleDIVA - â��DIVA: Data Intensive Visualization and Analysis

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