Abstract
Block compressed sensing (BCS) has great potential in image compression applications for its low storage requirement and low computational complexity. However, the sampling efficiency of traditional BCS is very poor since some blocks actually are not sparse enough to apply compressed sensing (CS). In order to improve the sampling efficiency, a novel BCS with random permutation and reweighted sampling (BCS-RP-RS) for image compression applications is proposed. In the proposed method, two effective strategies, including random permutation and reweighted sampling, are used simultaneously to guarantee all blocks of image signals sparse enough to apply CS. As a result, better sampling efficiency can be achieved. Simulation results show that the proposed approach improves the peak signal-to-noise ratio (PSNR) of the reconstructed-images significantly compared with the conventional BCS with random permutation (BCS-RP) approach.
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References
Pennbaker, W.B. and Mitchell, J.L., JPEG Still Image Data Compression Standard, New York: Springer-Verlag, 1993.
Skodras, A., Christopoulos, C., and Ebrahimi, T., The JPEG2000 still image compression standard, IEEE Signal Process. Mag., 2001, vol. 18, no. 9, pp. 36–58.
Gan, L., Block compressed sensing of natural images, Proc. of 2007 15th International Conference on Digital Signal Processing, Cardiff, 2007, pp. 403–406.
Donoho, D.L., Compressed sensing, IEEE Trans. Inf. Theory, 2006, vol. 52, no. 4, pp. 1289–1306.
Candes, E.J. and Tao, T., Decoding by linear programming, IEEE Trans. Inf. Theory, 2005, vol. 51, no. 12, pp. 4203–4215.
Candes, E.J., Romberg, J., and Tao, T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, 2006, vol. 52, no. 2, pp. 489–509.
Marco, F.D., Mark, A.D., Dharmpal, T., Jason, N.L., Ting, S., Kevin, F.K., and Richard, G.B., Single-pixel imaging via compressive sampling, IEEE Trans. Signal Mag., 2008, vol. 25, no. 2, pp. 83–91.
Mun, S. and Fowler, J.E., Block compressed sensing of images using directional transforms, Proc. of International Conference Image Processing, Cairo, 2009, pp. 3021–3024.
Gan, L., Do, T.T., and Tran, T.D., Fast compressive imaging using scrambled block Hadamard ensemble, Proc. of the European Signal Processing Conference, Lausanne, 2008, pp. 1–5.
Flower, J.E., Mun, S., and Tramel, E.W., Multiscale block compressed sensing with smoother projected Landweber reconstruction, Proc. of the 19th European Signal Processing Conference, Barcelona, 2011, pp. 564–568.
Gao, Z., Xiong, C., Zhou, C., and Wang, H., Compressive sampling with coefficients random permutations for image compression, Proc. International Conference on Multimedia and Signal Processing, 2011, pp. 321–324.
Yang, Y., Au, O.C., Fang, L., Wen, X., and Tang, W., Reweighted compressive sampling for image compression, Proc. of International Conference Picture Coding Symposium (PCS), 2009, pp. 89–92.
Chen, S., Donoho, D., and Saunders, M., Atomic decomposition by basis pursuit, SIAM Rev., 2001, vol. 43, no. 1, pp. 129–159.
Tropp, J. and Gilbert, A., Signal recovery from random measurements via orthogonal matching pursuit, IEEE Trans. Inf. Theory, 2007, vol. 53, no. 12, pp. 4655–4666.
Blumensath, T. and Davies, M., Iterative hard thresholding for compressed sensing, Appl. Comput. Harmonic Anal., 2009, vol. 27, no. 3, pp. 265–274.
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Cao, Y., Gong, W., Zhang, B. et al. Block Compressed Sensing Using Random Permutation and Reweighted Sampling for Image Compression Applications. Aut. Control Comp. Sci. 52, 118–125 (2018). https://doi.org/10.3103/S0146411618020025
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DOI: https://doi.org/10.3103/S0146411618020025