Abstract
Many algorithms have been developed to find sparse representation over redundant dictionaries or transform. This paper presents a novel method on compressive sensing (CS)-based image compression using sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the three levels of wavelet transform coefficients of the input image for compressive sampling. We have used three different measurement matrix as Gaussian matrix, Bernoulli measurement matrix and random orthogonal matrix. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct each level of wavelet transform separately. Experimental results demonstrate that the proposed method given better quality of compressed image than existing methods in terms of proposed image quality evaluation indexes and other objective (PSNR/UIQI/SSIM) measurements.
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