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Hierarchical tensor approximation of multi-dimensional visual data.

Qing Wu1, Tian Xia, Chun Chen

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. qingwu1@uiuc.edu

IEEE Transactions on Visualization and Computer Graphics
|November 13, 2007
PubMed
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This study introduces a novel hierarchical tensor approximation for compact visual data representation. The technique achieves superior compression and quality for multi-scale, inhomogeneous signals, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Data Compression
  • Signal Processing

Background:

  • Visual data is inherently multi-scale and spatially inhomogeneous.
  • Existing compression techniques struggle to efficiently represent these complex characteristics.
  • A need exists for advanced data representation methods for high-dimensional visual datasets.

Purpose of the Study:

  • To develop a compact data representation technique for multi-dimensional visual data.
  • To exploit the multi-scale and inhomogeneous nature of visual signals.
  • To improve compression ratios and data quality compared to current methods.

Main Methods:

  • A hierarchical tensor-based transformation is proposed.
  • Data is transformed into a hierarchy of signals to reveal multi-scale structures.

Related Experiment Videos

  • Spatially inhomogeneous structures are exposed using tensor approximation and pruning.

    • Main Results:

    • The hierarchical tensor approximation supports progressive transmission and partial decompression.
    • Experimental results show higher compression ratios and quality than wavelet transforms and single-level tensor approximation.
    • The technique was successfully applied to medical and scientific visualization, and texture synthesis.

    Conclusions:

    • The proposed hierarchical tensor approximation is an effective method for compressing multi-dimensional visual data.
    • This technique offers significant advantages in compression efficiency and data fidelity.
    • It has broad applicability in various visual data processing tasks.