Adaptive sampling for compressed sensing based image compression

• A jointly-reweighted block-based compressed sensing scheme.
• A generic measurement allocation algorithm to assign CS-measurements.
• Statistical parameters as allocation factors.
• Two solutions to implement the adaptive measurement allocation.
• Remarkable quality improvement over the traditional reweighted BCS scheme.

The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. In this paper, we focus on how to improve the sampling efficiency for CS-based image compression by using our proposed adaptive sampling mechanism on the block-based CS (BCS), especially the reweighted one. To achieve this goal, two solutions are developed at the sampling side and reconstruction side, respectively. The proposed sampling mechanism allocates the CS-measurements to image blocks according to the statistical information of each block so as to sample the image more efficiently. A generic allocation algorithm is developed to help assign CS-measurements and several allocation factors derived in the transform domain are used to control the overall allocation in both solutions. Experimental results demonstrate that our adaptive sampling scheme offers a very significant quality improvement as compared with traditional non-adaptive ones.