Compressed Sensing for Image Compression Using Wavelet Packet Analysis

Abstract

Compressed sensing is a recently developed technique that exploits the sparsity of naturally occurring signals and images to reduce the volume of the data using less number of samples, computing the sparsity of the signal. In the traditional/conventional approaches the images are acquired and compressed, where as compressed sensing aims to acquire the “compressed signals” with few numbers of samples and reconstruct the images. This will allow us to acquire the large ground/region with few numbers of input samples. This technique works on the assumption that natural signals/images have inherent sparsity. . In this algorithm, the original image is first decomposes with the wavelet packet to make it sparse, and then retains the low frequency coefficients in line with the optimal basis of the wavelet packet, meanwhile, makes random measurements of all the high frequency coefficients according to the compressed sensing theory, and last restores them with the orthogonal matching pursuit (OMP) method, and does the inverse transform of the wavelet packet to reconstruct the original image, to achieve the image compression.