Multiresolution compression and reconstruction

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...

• Type: Conference
• Year: 1997
• Where: VISUALIZATION
• Authors: Oliver G. Staadt, Markus H. Gross, Roger Weber
• Keywords: Geometric Reconstruction, Linear Approximation, Piecewise Linear Approximation, Visualization, VISUALIZATION 1997