Abstract
Although quantum image scaling algorithms have been widely studied in recent years, almost all of them require the quantum image to be enlarged or reduced simultaneously in the horizontal and vertical directions. However, the scaling schemes that enlarge the quantum image in one direction and shrink it in the other direction are rarely involved. In this paper, a quantum image scaling scheme based on the extension of the bilinear interpolation method is proposed to achieve asymmetric scaling over the two dimensions of a quantum image for the first time. Firstly, the improved novel-enhanced quantum representation of digital images (INEQR) is employed to represent a \( 2^{{n_{1} }} \times 2^{{n_{2} }} \) quantum image, and the bilinear interpolation is improved to use two adjacent pixels in the original image for interpolation. Then, the concrete circuits for the asymmetric scaling of quantum images are designed. Finally, the simulation results are given, and the complexity of the quantum circuits and the peak signal-to-noise ratio (PSNR) are used to quantitatively compare with the similar scheme proposed in another paper. The results show that the proposed scheme has lower computational complexity and better scaling effect than another scheme.
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Vlasov, A.Y.: Quantum computations and images recognition. arXiv:quant-ph/9703010. (1997)
Beach, G., Lomont, C., Cohen, C.: Quantum image processing (QuIP). In: Proceedings—Applied Imagery Pattern Recognition Workshop (2004)
Venegas-Andraca, S.E., Bose, S.: Quantum computation and image processing: New trends in artificial intelligence. In: IJCAI International Joint Conference on Artificial Intelligence (2003)
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)
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Annual ACM Symposium on Theory of Computing, pp. 212–219 (1996)
Latorre, J.I.: Image compression and entanglement. arXiv:quant-ph/0510031 (2005)
Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9, 1–11 (2010)
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, 63–84 (2011)
Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12, 2833–2860 (2013)
Li, H.S., Qingxin, Z., Lan, S., Shen, C.Y., Zhou, R., Mo, J.: Image storage, retrieval, compression and segmentation in a quantum system. Quantum Inf. Process. 12, 2269–2290 (2013)
Li, H.S., Zhu, Q., Zhou, R.G., Song, L., Yang, X.J.: Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state. Quantum Inf. Process. 13, 991–1011 (2014)
Yan, F., Iliyasu, A.M., Venegas-andraca, S.E.: A survey of quantum image representations. Quantum Inf. Process. 15, 1–35 (2016)
Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Fast geometric transformations on quantum images. IAENG Int. J. Appl. Math. 40, (2010)
Wang, J., Jiang, N., Wang, L.: Quantum image translation. Quantum Inf. Process. 14, 1589–1604 (2015)
Jiang, N., Wang, L.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14, 1559–1571 (2015)
Jiang, N., Wang, J., Mu, Y.: Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio. Quantum Inf. Process. 14, 4001–4026 (2015)
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)
Zhou, R.G., Hu, W., Fan, P., Ian, H.: Quantum realization of the bilinear interpolation method for NEQR. Sci. Rep. 7, (2017)
Zhou, R.G., Liu, X., Luo, J.: Quantum Circuit Realization of the Bilinear Interpolation Method for GQIR. Int. J. Theor. Phys. 56, 2966–2980 (2017)
Li, P., Liu, X.: Bilinear interpolation method for quantum images based on quantum Fourier transform. Int. J. Quantum Inf. 16, 1850031 (2018)
Zhou, R., Hu, W., Luo, G., Liu, X., Fan, P.: Quantum realization of the nearest neighbor value interpolation method for INEQR. Quantum Inf. Process. 17, 166 (2018)
Zhou, R.-G., Cheng, Y., Liu, D.: Quantum image scaling based on bilinear interpolation with arbitrary scaling ratio. Quantum Inf. Process. 18(9), 267 (2019)
Jiang, N., Wu, W.Y., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13, 1223–1236 (2014)
Jiang, N., Wang, L.: Analysis and improvement of the quantum Arnold image scrambling. Quantum Inf. Process. 13, 1545–1551 (2014)
Jiang, N., Wang, L., Wu, W.Y.: Quantum Hilbert Image Scrambling. Int. J. Theor. Phys. 53, 2463–2484 (2014)
Li, Y.C., Zhou, R., Xu, R.Q., Luo, J., Jiang, S.-X.: A quantum mechanics-based framework for EEG signal feature extraction and classification. IEEE Trans. Emerg. Top. Comput. (2020). https://doi.org/10.1109/tetc.2020.3000734
Iliyasu, A.M., Le, P.Q., Dong, F., Hirota, K.: Watermarking and authentication of quantum images based on restricted geometric transformations. Inf. Sci. (Ny) 186, 126–149 (2012)
Li, Y.C., Zhou, R.-G., Xu, R.Q., Luo, J., Hu, W.W.: A quantum deep convolutional neural network for image recognition. Quantum Sci. Technol. 5(4), 044003 (2020). https://doi.org/10.1088/2058-9565/ab9f93
Zhou, R.G., Hu, W., Fan, P.: Quantum watermarking scheme through Arnold scrambling and LSB steganography. Quantum Inf. Process. 16, 1–21 (2017)
Zhou, R.G., Wu, Q., Zhang, M.Q., Shen, C.Y.: Quantum image encryption and decryption algorithms based on quantum image geometric transformations. Int. J. Theor. Phys. 52, 1802–1817 (2013)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, New Jersey (2007)
Parker, J.A., Kenyon, R.V., Troxel, D.E.: Comparison of Interpolating Methods for Image Resampling. IEEE Trans. Med, Imaging (1983)
Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Strategies for designing geometric transformations on quantum images. Theor. Comput. Sci. 412, 1406–1418 (2011)
Thapliyal, H., Ranganathan, N.: Design of efficient reversible binary subtractors based on a new reversible gate. In: Proc. 2009 IEEE Comput. Soc. Annu. Symp. VLSI, ISVLSI 2009, pp. 229–234 (2009)
Thapliyal, H., Ranganathan, N.: A new design of the reversible subtractor circuit. In: Proc. IEEE Conf. Nanotechnol, pp. 1430–1435 (2011)
Islam, M.S., Rahman, M.M., Begum, Z., Hafiz, M.Z.: Low cost quantum realization of reversible multiplier circuit. Inf. Technol. J. 8, 208–213 (2009)
Ruiz-Perez, L., Garcia-Escartin, J.C.: Quantum arithmetic with the quantum Fourier transform. Quantum Inf. Process. 16(6), 152 (2017)
Kotiyal, S., Thapliyal, H., Ranganathan, N.: Circuit for reversible quantum multiplier based on binary tree optimizing ancilla and garbage bits. In: Proc. IEEE Int. Conf. VLSI Des, pp. 545–550 (2014)
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
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This work is supported by the National Key Research and Development Plan under Grant Nos. 2018YFC1200200 and 2018YFC1200205.
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Zhou, RG., Cheng, Y., Qi, X. et al. Asymmetric scaling scheme over the two dimensions of a quantum image. Quantum Inf Process 19, 343 (2020). https://doi.org/10.1007/s11128-020-02837-9
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DOI: https://doi.org/10.1007/s11128-020-02837-9