Block Compressed Sensing Using Random Permutation and Reweighted Sampling for Image Compression Applications

Abstract

Block compressed sensing (BCS) has great potential in image compression applications for its low storage requirement and low computational complexity. However, the sampling efficiency of traditional BCS is very poor since some blocks actually are not sparse enough to apply compressed sensing (CS). In order to improve the sampling efficiency, a novel BCS with random permutation and reweighted sampling (BCS-RP-RS) for image compression applications is proposed. In the proposed method, two effective strategies, including random permutation and reweighted sampling, are used simultaneously to guarantee all blocks of image signals sparse enough to apply CS. As a result, better sampling efficiency can be achieved. Simulation results show that the proposed approach improves the peak signal-to-noise ratio (PSNR) of the reconstructed-images significantly compared with the conventional BCS with random permutation (BCS-RP) approach.