COMPRESSING UNSTRUCTURED MESH DATA USING SPLINE FITS, COMPRESSED SENSING, AND REGRESSION METHODS

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.