Wenli Li, W. Randolph Franklin, Salles V. G. de Magalhães, Marcus V. A. Andrade, and David L. Hedin. 3D segmented ODETLAP compression. 2016.
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We propose a segmented ODETLAP compression algorithm to increase speed, and show that it is usually better in the maximum absolute error than JP3D for 3D datasets. Overdetermined Laplacian partial differential equations (ODETLAP) is a spatial approximation and data compression method. We use the CUSP library to accelerate ODETLAP approximation and the speedup is about 7 times on a GPU over a CPU core. Segmented ODETLAP compression is faster and uses less memory than unsegmented ODETLAP compression. We use the algorithm to compress several atmospheric datasets and an MRI dataset. For evaluation, we also compress the datasets using JPEG 2000 Part 10 JP3D. The results show that the compressed size of the algorithm is about 60\% that of JP3D for the same maximum absolute error.

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