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   -> Volume 6, Issue 7

Preprint: Multiresolution Compression and Reconstruction
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Oliver Staadt (

PostPosted: Fri Jul 11, 1997 3:45 pm    
Subject: Preprint: Multiresolution Compression and Reconstruction
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#6 Preprint: Multiresolution Compression and Reconstruction

Title: Multiresolution Compression and Reconstruction

Authors: Oliver G. Staadt, Markus H. Gross and Roger Weber

To appear in: Proceedings of the IEEE Visualization Conference 97


Abstract: This paper presents a framework for multiresolution
compression and geometric reconstruction of arbitrarily dimensioned
data designed for distributed applications. Although being restricted
to uniform sampled data, our versatile approach enables the handling
of a large variety of real world elements. Examples include
nonparametric, parametric and implicit lines, surfaces or volumes, all
of which are common to large scale data sets. The framework is based
on two fundamental steps: Compression is carried out by a remote
server and generates a bitstream transmitted over the underlying
network. Geometric reconstruction is performed by the local client and
renders a piecewise linear approximation of the data. More precisely,
our compression scheme consists of a newly developed pipeline starting
from an initial B-spline wavelet precoding. The fundamental properties
of wavelets allow progressive transmission and interactive control of
the compression gain by means of global and local oracles. In
particular we discuss the problem of oracles in semiorthogonal
settings and propose sophisticated oracles to remove unimportant
coefficients. In addition, geometric constraints such as boundary
lines can be compressed in a lossless manner and are incorporated into
the resulting bit-stream. The reconstruction pipeline performs a
piecewise adaptive linear approximation of data using a fast and easy
to use point removal strategy which works with any subsequent
triangulation technique. As a result, the pipeline renders line
segments, triangles or tetrahedra. Moreover, the underlying
continuous approximation of the wavelet representation can be
exploited to reconstruct implicit functions, such as isolines and
isosurfaces more smoothly and precisely than commonplace
methods. Although it scales straightforwardly to higher dimensions the
performance of our framework is illustrated with results achieved on
data very popular in practice: parametric curves and surfaces, digital
terrain models, and volume data.

|Oliver Staadt e-mail: |
|Computer Graphics Group phone: +41-1-632-7122 |
|Computer Science Department fax: +41-1-632-1172 |
|Swiss Federal Institute of Technology ETH Zurich, CH |
|URL: |
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