A New Approach to the Block-based Compressive Sensing

The traditional block-based compressive sensing (BCS) approach considers the image to be segmented. However, there is not much literature available on how many numbers of blocks or segments per image would be the best choice for the compression and recovery methods. In this article, we propose a BCS method to find out the optimal way of image retrieval, and the number of the blocks to which into image should be divided. In the theoretical analysis, we analyzed the effect of noise under compression perspective and derived the range of error probability. Experimental results show that the number of blocks of an image has a strong correlation with the image recovery process. As the sampling rate M/N increases, we can find the appropriate number of image blocks by comparing each line.