2-D geometric signal compression method based on compressed sensing

Abstract

This paper provides a compression method of two-dimensional contour model based on compressed sensing. First, this method gets the 2-D geometric signal through discrete representing the two-dimensional contour model. Then, construct a basis using Laplace operator of the Two-dimensional contour model, thus we get the sparse representation of the 2-D geometric signal based on this basis. Last, we complete compressing the Two-dimensional contour model, through random sampling geometry signals based on Compressed Sensing. In the recovery process, we reconstruct the 2-D geometric signal through optimizing 1-norm of the sparse signal. This method completed the compression of Two-dimensional contour model in the sampling process. Experimental results show that the compression ratio of this method is high, restore effect is good and is suitable for large-scale data compression.