article

Out-of-core tensor approximation of multi-dimensional matrices of visual data

Authors:

Hongcheng Wang,

Narendra AhujaAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 24, Issue 3

Pages 527 - 535

https://doi.org/10.1145/1073204.1073224

Published: 01 July 2005 Publication History

Abstract

Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.

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Cited By

Tsai YAbu Bakar M(2024)HMK-CTA: A Hierarchical Multidimensional Representation for Visual DatasetsProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670954(1-10)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670954
Glisic SLorenzo B(2024)Quantum computing and neuroscience for 6G/7G networks: SurveyIntelligent Systems with Applications10.1016/j.iswa.2024.20034623(200346)Online publication date: Sep-2024
https://doi.org/10.1016/j.iswa.2024.200346
Yu ZLi DZhang ZLuo WLiu YWang ZYuan L(2021)Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)Geoscientific Model Development10.5194/gmd-14-875-202114:2(875-887)Online publication date: 11-Feb-2021
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Index Terms

Out-of-core tensor approximation of multi-dimensional matrices of visual data
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Texturing
    2. Rendering

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 24, Issue 3

July 2005

826 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1073204

Issue’s Table of Contents

Copyright © 2005 ACM.

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Publication History

Published: 01 July 2005

Published in TOG Volume 24, Issue 3

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Cited By

Tsai YAbu Bakar M(2024)HMK-CTA: A Hierarchical Multidimensional Representation for Visual DatasetsProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670954(1-10)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670954
Glisic SLorenzo B(2024)Quantum computing and neuroscience for 6G/7G networks: SurveyIntelligent Systems with Applications10.1016/j.iswa.2024.20034623(200346)Online publication date: Sep-2024
https://doi.org/10.1016/j.iswa.2024.200346
Yu ZLi DZhang ZLuo WLiu YWang ZYuan L(2021)Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)Geoscientific Model Development10.5194/gmd-14-875-202114:2(875-887)Online publication date: 11-Feb-2021
https://doi.org/10.5194/gmd-14-875-2021
Kurt M(2021)GenSSS: a genetic algorithm for measured subsurface scattering representationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01800-037:2(307-323)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s00371-020-01800-0
Pajarola RSuter SBallester-Ripoll RYang H(2021)Tensor Approximation for Multidimensional and Multivariate DataAnisotropy Across Fields and Scales10.1007/978-3-030-56215-1_4(73-98)Online publication date: 11-Feb-2021
https://doi.org/10.1007/978-3-030-56215-1_4
Chen YLouri A(2020)An Approximate Communication Framework for Network-on-ChipsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2020.296806831:6(1434-1446)Online publication date: 1-Jun-2020
https://doi.org/10.1109/TPDS.2020.2968068
Díaz JMarton FGobbetti E(2020)Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionariesComputers & Graphics10.1016/j.cag.2020.03.002Online publication date: Mar-2020
https://doi.org/10.1016/j.cag.2020.03.002
Song ZWoodruff DZhong PChan T(2019)Relative error tensor low rank approximationProceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3310435.3310607(2772-2789)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.5555/3310435.3310607
An JSong YGuo YMa XZhang X(2019)Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality ReductionRemote Sensing10.3390/rs1115182211:15(1822)Online publication date: 4-Aug-2019
https://doi.org/10.3390/rs11151822
Miandji EHajisharif SUnger J(2019)A Unified Framework for Compression and Compressed Sensing of Light Fields and Light Field VideosACM Transactions on Graphics10.1145/326998038:3(1-18)Online publication date: 17-May-2019
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