Abstract
This paper is concerned with the feasibility of the classical nearest-neighbor interpolation based on flexible representation of quantum images (FRQI) and novel enhanced quantum representation (NEQR). Firstly, the feasibility of the classical image nearest-neighbor interpolation for quantum images of FRQI and NEQR is proven. Then, by defining the halving operation and by making use of quantum rotation gates, the concrete quantum circuit of the nearest-neighbor interpolation for FRQI is designed for the first time. Furthermore, quantum circuit of the nearest-neighbor interpolation for NEQR is given. The merit of the proposed NEQR circuit lies in their low complexity, which is achieved by utilizing the halving operation and the quantum oracle operator. Finally, in order to further improve the performance of the former circuits, new interpolation circuits for FRQI and NEQR are presented by using Control-NOT gates instead of a halving operation. Simulation results show the effectiveness of the proposed circuits.
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Benioff, P.: The computer as a physical system: a microscopic quantum mechanical Hamiltonian models of computers as represented by Turing machines. J. Stat. Phys. 22(5), 563–591 (1980)
Feynman, R.P.: Simulating physics with computers. Int. J. Theor. Phys. 21(6/7), 467–488 (1982)
Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. Proc. SPIE Conf. Quantum Inf. Comput. 5105, 137–147 (2003)
Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9(1), 1–11 (2010)
Latorre, J.I.: Image compression and entanglement. arXiv:quant-ph/0510031 (2005)
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 (2010)
Sun,B., Le, P.Q., Iliyasu, A.M., et al.: A multi-channel representation for images on quantum computers using the RGB \(\alpha \) color space. In: Proceedings of the IEEE 7th International Symposium on Intelligent Signal Processing, pp 1–6 (2011)
Zhang, Y., Lu, K., Gao, Y.H., Xu, K.: A novel quantum representation for log-polar images. Quantum Inf. Process. 12(9), 3103–3126 (2013)
Zhang, Y., Lu, K., Gao, Y.H., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013)
Iliyasu, A.M., Le, P.Q., Dong, F., et al.: Watermarking and authentication of quantum images based on restricted geometric transformation. Inf. Sci. 186(1), 126–149 (2012)
Zhang, W.W., Gao, F., Liu, B., Jia, H.Y., Wen, Q., Chen, H.: A quantum watermark protocol. Int. J. Theor. Phys. 52(2), 504–513 (2013)
Zhang, W.W., Gao, F., Liu, B., Wen, Q., Chen, H.: A watermark strategy for quantum images based on quantum Fourier transform. Quantum Inf. Process. 12(2), 793–803 (2013)
Zhou, R.G., Wu, Q., Zhang, M.Q., et al.: Quantum image encryption and decryption algorithms based on quantum image geometric transformations. Int. J. Theor. Phys. 52(6), 1802–1817 (2013)
Yang, Y.G., Xia, J., Jia, X., et al.: Novel image encryption/decryption based on quantum Fourier transform and double phase encoding. Quantum Inf. Process. 12(11), 3477–3493 (2013)
Yang, Y.G., Jia, X., Sun, S., et al.: Quantum cryptographic algorithm for color images using quantum Fourier transform and double random-phase encoding. Inf. Sci. 277, 445–457 (2014)
Song, X.H., Wang, S., El-latif, A.A.A., Niu, X.M.: Quantum image encryption based on restricted geometric and color transformations. Quantum Inf. Process. 13(8), 1765–1787 (2014)
Jiang, N., Wu, W.Y., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13(5), 1223–1236 (2014)
Jiang, N., Wang, L.: Analysis and improvement of the quantum Arnold image scrambling. Quantum Inf. Process. 13(5), 1545–1551 (2014)
Jiang, N., Wang, L., Wu, W.Y.: Quantum Hilbert image scrambling. Int. J. Theor. Phys. 53(7), 2463–2484 (2014)
Le, P.Q., Iliyasu, A.M., Dong, F.Y., Hirota, K.: Fast geometric transformations on quantum images. Int J. Appl. Math. 40(3), 113–123 (2010)
Nielson, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2010)
Fijany, A., Williams, C.: Quantum wavelet transform: fast algorithm and complete circuits. arXiv:quant-ph/9809004 (1998)
Klappenecker, A., Rötteler, M.: Discrete cosine transforms on quantum computers. In: IEEE8-EURASIP Symposium on Image and Signal Processing and Analysis (ISPA01), Pula, Croatia, pp. 464–468 (2001)
Tseng, C.C., Hwang, T.M.: Quantum circuit design of \(8 \times 8\) discrete cosine transform using its fast computation flow graph. In: IEEE International Symposium on Circuits and Systems, 2005 (ISCAS 2005), pp. 828–831 (2005)
Wang, J., Jiang, N., Wang, L.: Quantum image translation. Quantum Inf. Process. 14(5), 1589–1604 (2014)
Jiang, N., Wang, L.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14(5), 1559–1571 (2014)
Zhang, Y., Lu, K., Xu, K., Gao, Y.H.: Local feature point extraction for quantum images. Quantum Inf. Process. 14(5), 1573–1588 (2014)
Barenco, A., Bennett, C.H., Cleve, R., DiVincenzp, D.P., Margolus, N., Shor, P., Sleator, T., Smolin, J.A., Weinfurther, H.: Elementary gates for quantum computation. Phys. Rev. A. 52(5), 3457 (1995)
Acknowledgments
This work is supported by the National Science Foundation of China (61301099, 60832010, 61501148 and 61361166006). We thank the previous researchers’ work about nearest-neighbor interpolation method for INEQR. Thanks are due to many anonymous reviewers for their assistance with the discussion about the improved circuits and the simulation results.
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Sang, J., Wang, S. & Niu, X. Quantum realization of the nearest-neighbor interpolation method for FRQI and NEQR. Quantum Inf Process 15, 37–64 (2016). https://doi.org/10.1007/s11128-015-1135-5
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DOI: https://doi.org/10.1007/s11128-015-1135-5