Perceptual sparse representation for compressed sensing of image

Most traditional image coding schemes based on compressed sensing exploited the sparse domain in fixed bases and less consider the image non-stationary characteristic and human visual characteristic, which leads to poor performance of the reconstruction. In this paper, we proposed a novel sparse CS scheme combined with just-noticeable difference (JND) Model and random permutation. Firstly, the DCT-based JND profile has been utilized to remove the perceptual redundancies which also makes the signal sparser, then the random permutation is adopted to balance the sparsity of each block in image. Experimental results show that the proposed perceptual sparse algorithm outperforms some existing approaches, and it can achieve better subjective and objective image quality compared to other algorithms when the sampling rate is above 0.3.