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Quantum Hilbert Image Scrambling

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Abstract

Analogies between quantum image processing (QIP) and classical one indicate that quantum image scrambling (QIS), as important as quantum Fourier transform (QFT), quantum wavelet transform (QWT) and etc., should be proposed to promote QIP. Image scrambling technology is commonly used to transform a meaningful image into a disordered image by permutating the pixels into new positions. Although image scrambling on classical computers has been widely studied, we know much less about QIS. In this paper, the Hilbert image scrambling algorithm, which is commonly used in classical image processing, is carried out in quantum computer by giving the scrambling quantum circuits. First, a modified recursive generation algorithm of Hilbert scanning matrix is given. Then based on the flexible representation of quantum images, the Hilbert scrambling quantum circuits, which are recursive and progressively layered, is proposed. Theoretical analysis indicates that the network complexity scales squarely with the size of the circuit’s input n.

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References

  1. Vedral, V., Barenco, A., Ekert, A.: Quantum networks for elementary arithmetic operations. Phys. Rev. A 54(1), 147–153 (1996)

    Article  ADS  MathSciNet  Google Scholar 

  2. Feynman, R.: Quantum mechanical computers. Found. Phys. 16(6), 507–531 (1986)

    Article  ADS  MathSciNet  Google Scholar 

  3. Nielson, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  4. Williams, C.: Explorations in quantum computing. In: Texts in Computer Science (2011)

  5. Caraiman, S., Manta, V.: Image processing using quantum computing. In: International Conference on 16th System Theory, Control and Computing (ICSTCC), pp 1–6 (2012)

  6. Beach, G., Lomont, C., Cohen, C.: Quantum image processing (QuIP). In: 32nd Applied Imagery Pattern Recognition Workshop, pp 39–44 (2003)

  7. Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum system. J. Quantum. Inf. Process. 9(1), 1–11 (2010)

    Article  MathSciNet  Google Scholar 

  8. Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Proceedings of the SPIE Conference on Quantum Information and Computation, pp 137–147 (2003)

  9. Latorre, J.I.: Image Compression and Entanglement. (2005). arXiv:quantph/0510031

  10. Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression and processing operations. Quantum Inf. Process. 10(1), 63–84 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  11. Zhang, Y., Lu, K., Gao, Y.-H., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(12), 2833–2860 (2013)

  12. Fijany, A., Williams, C.: Quantum Wavelet Transform: Fast Algorithm and Complete Circuits (1998). arXiv:quant-ph/9809004

  13. Klappenecker, A., Roetteler, M.: Discrete cosine transforms on quantum computers. In: IEEER8-EURASIP Symposium on Image and Signal Processing and Analysis (ISPA01), pp. 464–468. Pula (2001)

  14. Tseng, C., Hwang, T.: Quantum circuit design of 8 × 8 discrete cosine transforms using its fast computation flow graph. In: IEEE International Symposium on Circuits and Systems, pp 828–831 (2005)

  15. Lomont, C.: Quantum Convolution and Quantum Correlation Algorithms are Physically Impossible (2003). arXiv:quant-ph/0309070

  16. Zhang, W.-W., Gao, F., Liu, B., et al.: A watermark strategy for quantum images based on quantum Fourier transform. Quantum Inf. Process. 12(4), 793–803 (2013)

  17. Weiwei, Z., Fei, G., Bing, L., et al.: A quantum watermark protocol. Int. J. Theor. Phys. 52, 504–513 (2013)

  18. Iliyasu, A. M., Le, P. Q., Dong, F., Hirota, K.: Watermarking and authentication of quantum images based on restricted geometric transformations. Inf. Sci. 186, 126–149 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  19. Hu, M.-Y., Tian, X.-L., Xia, S.-W., et al.: Image scrambling based on 3-D Hilbert curve. In: 3rd International Congress on Image and Signal Processing, vol. 1, pp. 147–149 (2010)

  20. Guo, J.-M., Yang, Y., Wang, N.: Chaos-based gray image watermarking algorithm. In: International Conference on Uncertainty Reasoning and Knowledge Engineering, vol.1, pp. 158–160 (2011)

  21. Lien, B.K.: Robust data hiding by Hilbert curve decomposition. In: IEEE Intelligent Information Hiding and Multimedia Signal Processing, pp 937–940 (2009)

  22. Sun Y.-Y., Kong R.-Q., Wang X.-Y.: An image encryption algorithm utilizing Mandelbrot set. In: International Workshop on Chaos-Fractal Theory and Its Applications, pp 170–173 (2010)

  23. Moreno, J., Otazu, X.: Image compression algorithm based on Hilbert scanning of embedded quadtrees: an introduction of the hi-set coder. In: IEEE International Conference on Multimedia and Expo, pp 1–6 (2011)

  24. Lin, X.-H., Cai, L.-D.: Scrambling research of digital image based on Hilbert curve. Chinese J. Stereology Image Anal. 9(4), 224–227 (2004)

  25. Shang, Z., Ren, H., Zhang, J.: A block location scrambling algorithm of digital image based on Arnold transformation. In: 9th International Conference for Young Computer Scientists, pp 2942–2947 (2008)

  26. Zou, W.-G., Huang, J.-Y., Zhou C.-Y.: Digital image scrambling technology based on two dimension Fibonacci transformation and its periodicity. In: 3rd International Symposium on Information Science and Engineering, pp 415–418 (2010)

  27. Wen M.-G., Huang S.-C., Han C.-C.: An information hiding scheme using magic squares. In: IEEE International Conference on Broadband, Wireless Computing, Communication and Applications, pp 556–560 (2010)

  28. Wang, S., Xu, X.-S.: A new algorithm of Hilbert scanning matrix and its MATLAB program. J. Image Graph. 11(1), 119–122 (2006)

  29. Le, P. Q., Iliyasu, A. M., Dong, F. Y., Hirota, K.: Fast geometric transformation on quantum images. IAENG Int. J. Appl. Math. 40(3), 113–123 (2010)

    MATH  MathSciNet  Google Scholar 

  30. Jiang, N., Wu, W.-Y., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. accepted.

  31. Peano, G.: Sur une courbe, qui remplit toute une aire plane. Math. Ann. 36(1), 157–160 (1890)

    Article  MATH  MathSciNet  Google Scholar 

  32. Hilbert, D.: Ueber die stetige Abbildung einer Linie auf ein Flächenstück. Math. Ann. 38(3), 459–460 (1891)

    Article  MATH  MathSciNet  Google Scholar 

  33. Kamata, S., Eason, R.O., Bandou, Y.: A new algorithm for N-dimensional Hilbert scanning. IEEE Trans. Image Process. 8(7), 964–973 (1999)

    Article  ADS  MATH  MathSciNet  Google Scholar 

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Acknowledgments

This work is supported by the Beijing Municipal Education Commission Science and Technology Development Plan under Grants No. KM201310005021, KZ201210005007, the Fundamental Research Funds for the Central Universities under Grants No. 2012JBM041, and the Graduate Technology Fund of BJUT under Grants No. YKJ-2013-10282.

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Correspondence to Nan Jiang.

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Jiang, N., Wang, L. & Wu, WY. Quantum Hilbert Image Scrambling. Int J Theor Phys 53, 2463–2484 (2014). https://doi.org/10.1007/s10773-014-2046-4

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  • DOI: https://doi.org/10.1007/s10773-014-2046-4

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