A novel algorithm on adaptive image compressed sensing with sparsity fitting

When the image is compressed adaptively with compressed sensing theory, the determination of sampling rate and sparsity threshold are highly subjective. In order to solve the problem, an accurately adaptive sampling algorithm with sparsity fitting is proposed in this paper. This algorithm determines the minimum sampling rate under certain sparsity to meet the PSNR requirements by iteration, and an optimal objective function of sampling rate choices is obtained by fitting sparsity and sampling rate data with the method of least squares. The adaptive sampling algorithm is simulated based on TVAL3. Experimental results show that the PSNR values of reconstructed images are higher than that with the same fixed sampling rate algorithm, and the PSNR increment of clear texture distinction images can reach at least 3.5dB. Compared to the roughly adaptive compression method, when the average sampling rate is lower, the reconstructed image obtains a higher PSNR value.