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Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods

Citation Author(s):
Chandrika Kamath, Ya Ju Fan
Submitted by:
Chandrika Kamath
Last updated:
3 December 2018 - 5:12pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Chandrika Kamath
Paper Code:
GS-P.6.1

Abstract 

Abstract: 

Compressing unstructured mesh data from computer simulations poses several challenges that are not encountered in the compression of images or videos. Since the spatial locations of the points are not on a regular grid, as in an image, it is difficult to identify near neighbors of a point whose values can be exploited for compression. In this paper, we investigate how three very different methods --- spline fits, compressed sensing, and kernel regression --- compare in terms of the reconstruction accuracy and reduction in data size when applied to a practical problem from a plasma physics simulation.

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