In matrix permutation-based Block Compressive Sensing (BCS), matrix permutation strategy is introduced, which makes the complex sub-blocks and sparse sub-blocks change to the middle level of sparsity and reduces the blocking artifacts when sampling with the single sampling rate. However there is still a problem of poor sparsity balance among blocks. In order to get better reconstruction effect, a Ripple Matrix Permutation-based sparsity balanced BCS (BCS-RMP) algorithm was proposed. Firstly, an image was pre-processed by matrix replacement before sampling, and the sparsity of each sub-block of the image was equalized by ripple permutation matrix. Then, a same measurement matrix was used to sample the sub-blocks and reconstruct them on the decoding side. Finally, the final reconstructed image was obtained by inverse transformation of reconstruction results by the ripple permutation inverse matrix. The simulation results show that, compared with the existing matrix replacement algorithms, the proposed ripple matrix permutation algorithm can effectively improve the quality of image reconstruction, and it can reflect the details more accurately when choosing appropriate sub-block size and sampling rate.