Perceptual Compressive Sensing Scheme Based on Human Vision System

Compressive Sensing (CS) theory has gained widespread attention due to its advantage of breaking through the limits of Nyquist sampling theorem. To make the CS more adaptive, some works based on the human vision system (HVS) have been conducted, but incurring some other problems at the same time, such as additional sensors, higher computation power and added experiments. To solve these problems a perceptual CS scheme based on the masking effect of human eyes towards image textures is proposed in this paper. It is consisted of three steps. First the image signal is represented sparsely through DWT transformation, and the masking matrix for different brightness changing areas is computed based on the DWT decomposition. Second the CS measurement with the masking matrix is performed on the image signal to get a lower-dimension sampling. And finally the image is reconstructed from its sparse sampling data. Through several experiments we can see that the proposed scheme performs better in both visual quality and PSNR assessment than common CS without any increasing of the computational complexity.