Research on Spatial Statistical Data Compression Algorithm Based on Point Cloud Clustering

Shao Ming Pan; Hong Li; Ge Tang

doi:10.4028/www.scientific.net/AMM.543-547.1619

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The Gel'fand Approximations on Multivariate Functions in the Deterministic and Monte Carlo Settings
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Research on Spatial Statistical Data Compression...

Research on Spatial Statistical Data Compression Algorithm Based on Point Cloud Clustering

Abstract:

The spatial information service system have the characteristics of a large amount of massive data and a large number of users, so the compression algorithm for statistical data will affects the system performance of spatial data services. A compression algorithm for spatial statistical data based on point cloud clustering is proposed in this paper to solve the above-mentioned problems. The access rule of the spatial data will be mapped into a point cloud. And the part of points will be deleted according to the clustering gradient of point cloud. Then the statistical data is compressed by run-length coding and spatial clustering extraction. The experimental results show that the algorithm have a high compression efficiency.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

1619-1622

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.1619

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Online since:

March 2014

Authors:

Shao Ming Pan*, Hong Li, Ge Tang

Keywords:

Data Compression, Point Clouds, Spatial Clustering, Spatial Statistical Data

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* - Corresponding Author

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