From compression to compressed sensing

Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms C_R, parametrized by rate R, for a compact class of signals Q ⊂ Rⁿ. The set of natural images and JPEG2000 at different rates are examples of Q and C_r, respectively. We establish a connection between the rate-distortion performance of C_R, and the number of linear measurement required for successful recovery in CS. We then propose compressible signal pursuit (CSP) algorithm and prove that, with high probability, it accurately and robustly recovers signals from an underdetermined set of linear measurements.