Improved compressive sensing for grayscalelmages with zigzag scanning and block DCT

The optimization of bandwidth and storage are the major issues in image processing applications like compression, watermarking, segmentation, video streaming and video conferencing. The compressive sensing can become prominent tool for these issues because it can sample the signal with much lesser sample rate than twice of the maximum frequency of the signal and reconstruct the signal similar to original signal. In this paper, the compressive sensing is applied on the zigzag pattern of block DCT coefficients for image coding and the image is reconstruct with Orthogonal Matched Pursuit (OMP) recovery tool. Moreover, the performance of proposed scheme is compared with existing technique which applies the compressive sensing on block DCT coefficients for image coding and recovers the signal using l1 minimization recovery tool. The comparative analysis shows that the proposed method outperforms the existing algorithm which uses either line based or block based DCT. The proposed algorithm gives increased PSNR value of reconstructed image by 4 dB to 6 dB as compared to existing algorithm.