Share Email Print

Proceedings Paper

High-dimensional data compression via PHLCT
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top