Robust compression using Compressive Sensing (CS)

Abstract

This work introduces a new technique of robust compression on signals and images, known as Compressive Sensing (CS). It is a new and advanced technique which can reconstruct sparse signals from a few random acquired samples, achieved to avoid the Nyquist's criteria. Reconstruction of a signal with just few data is a hard task converted in a lineal optimization process with various ways to find out the solution. Widely-used for research at present, CS is a useful tool of sampling/compression that only works with sparse signals, moreover we were able to implement CS in some kind of signals and images, but it is necessary to "rewrite it" according to a few meaningful terms, which can be obtained using properties time/frequency/energy, etc. The derived, cosine discrete transform (DCT), Fourier and Wavelet analysis are some of the tools to sparse convertion of signals and images.