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Block Compressed Sensing Using Random Permutation and Reweighted Sampling for Image Compression Applications

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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

  1. Pennbaker, W.B. and Mitchell, J.L., JPEG Still Image Data Compression Standard, New York: Springer-Verlag, 1993.

    Google Scholar 

  2. 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.

    Article  MATH  Google Scholar 

  3. Gan, L., Block compressed sensing of natural images, Proc. of 2007 15th International Conference on Digital Signal Processing, Cardiff, 2007, pp. 403–406.

    Chapter  Google Scholar 

  4. Donoho, D.L., Compressed sensing, IEEE Trans. Inf. Theory, 2006, vol. 52, no. 4, pp. 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  5. Candes, E.J. and Tao, T., Decoding by linear programming, IEEE Trans. Inf. Theory, 2005, vol. 51, no. 12, pp. 4203–4215.

    Article  MathSciNet  MATH  Google Scholar 

  6. 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.

    Article  MathSciNet  MATH  Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. 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.

    Google Scholar 

  9. 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.

    Google Scholar 

  10. 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.

    Google Scholar 

  11. 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.

    Google Scholar 

  12. 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.

    Google Scholar 

  13. Chen, S., Donoho, D., and Saunders, M., Atomic decomposition by basis pursuit, SIAM Rev., 2001, vol. 43, no. 1, pp. 129–159.

    Article  MathSciNet  MATH  Google Scholar 

  14. 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.

    Article  MathSciNet  MATH  Google Scholar 

  15. Blumensath, T. and Davies, M., Iterative hard thresholding for compressed sensing, Appl. Comput. Harmonic Anal., 2009, vol. 27, no. 3, pp. 265–274.

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Bo Zhang.

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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

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