Image Compression and Sparsity Measurement by using Multilevel and Different Wavelet Functions

This paper proposes the image compression techniques by using multilevel wavelet transform with different wavelet filters and sparsity measurement, which is the essential requirement in the arena of Compressive Sensing (CS). CS is an innovative technique, which is used to state that one can reconstruct the signals from considerably fewer samples as compared to the customary method called Nyquist Sampling theorem. When a vector or matrix has mostly zero pixel value, then it is called sparsity of a matrix and multilevel wavelet transform is used for this purpose.