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Proceedings Paper

High-dimensional data compression via PHLCT
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Paper Abstract

The polyharmonic local cosine transform (PHLCT), presented by Yamatani and Saito in 2006, is a new tool for local image analysis and synthesis. It can compress and decompress images with better visual fidelity, less blocking artifacts, and better PSNR than those processed by the JPEG-DCT algorithm. Now, we generalize PHLCT to the high-dimensional case and apply it to compress the high-dimensional data. For this purpose, we give the solution of the high-dimensional Poisson equation with the Neumann boundary condition. In order to reduce the number of coefficients of PHLCT, we use not only d-dimensional PHLCT decomposition, but also d-1, d-2, . . . , 1 dimensional PHLCT decompositions. We find that our algorithm can more efficiently compress the high-dimensional data than the block DCT algorithm. We will demonstrate our claim using both synthetic and real 3D datasets.

Paper Details

Date Published: 20 September 2007
PDF: 10 pages
Proc. SPIE 6701, Wavelets XII, 670127 (20 September 2007); doi: 10.1117/12.733226
Show Author Affiliations
Zhihua Zhang, Univ. of California, Davis (United States)
Naoki Saito, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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