|
|
Quantum color image scaling based on bilinear interpolation |
Chao Gao(高超)1,2, Ri-Gui Zhou(周日贵)1,2,†, and Xin Li(李鑫)1,2 |
1 College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China; 2 Research Center of Intelligent Information Processing and Quantum Intelligent Computing, Shanghai 201306, China |
|
|
Abstract As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the $2^{n_{1}}\times 2^{n_{2}}$ quantum color image scaling. Firstly, the improved novel quantum representation of color digital images (INCQI) is employed to represent a $2^{n_{1}}\times 2^{n_{2}}$ quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is analyzed. Finally, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.
|
Received: 12 May 2022
Revised: 14 August 2022
Accepted manuscript online: 05 September 2022
|
PACS:
|
03.67.-a
|
(Quantum information)
|
|
03.67.Ac
|
(Quantum algorithms, protocols, and simulations)
|
|
03.67.Lx
|
(Quantum computation architectures and implementations)
|
|
07.05.Pj
|
(Image processing)
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 6217070290) and Shanghai Science and Technology Project (Grant Nos. 21JC1402800 and 20040501500). |
Corresponding Authors:
Ri-Gui Zhou
E-mail: rgzhou@shmtu.edu.cn
|
Cite this article:
Chao Gao(高超), Ri-Gui Zhou(周日贵), and Xin Li(李鑫) Quantum color image scaling based on bilinear interpolation 2023 Chin. Phys. B 32 050303
|
[1] Feynman R P 1982 Int. J. Theor. Phys. 21 467 [2] Deutsch D 1985 Proc. Royal Soc. London Ser. A 400 97 [3] Shor P W 1994 Proceedings of 35th Annual Symposium on Foundations of Computer Science pp. 124-134 [4] Grover L K 1996 Proceedings of the 28th Annual ACM symposium on the Theory of Computing pp. 212-219 [5] Venegas-Andraca S and Bose S 2003 Proc. SPIE 5105, Quantum Information and Computation August 4 2003 [6] Latorre J I 2005 arXiv: quant-ph/0510031 [hep-ph] [7] Venegas-Andraca S and Ball J 2010 Quantum Inform. Proc. 9 1 [8] Le P Q, Dong F and Hirota K 2011 Quantum Inform. Proc. 10 63 [9] Zhang Y, Lu K, Gao Y and Wang M 2013 Quantum Inform. Proc. 12 2833 [10] Li H S, Zhu Q, Zhou R G, Song L and Yang X 2014 Quantum Inform. Proc. 13 991 [11] Sang J, Wang S and Li Q 2017 Quantum Inform. Proc. 16 42 [12] Le P Q, Iliyasu A M, Dong F Y and Hirota K 2010 IAENG Int. J. Appl. Math. 40 113 [13] Hirota K, Dong F Y, Le P Q and Iliyasu A M 2011 J. Adv. Comput. Intell. Intell. Informatics 15 698 [14] Zhang Y, Lu K, Xu K, Gao Y H and Wilson R 2015 Quantum Inform. Proc. 14 1573 [15] Wang J, Jiang N and Wang L 2015 Quantum Inform. Proc. 14 1589 [16] Jiang N and Luo W 2015 Quantum Inform. Proc. 14 1559 [17] Jiang N, Wang J and Mu Y 2015 Quantum Inform. Proc. 14 4001 [18] Zhou R G, Tan C Y, Fan P 2017 Mod. Phys. Lett. B 31 1750184 [19] Chen G L and Song X H 2021 Data Science 1451 453 [20] Jiang N, Wu W Y and Wang L 2014 Quantum Inform. Proc. 13 1223 [21] Jiang N and Wang L 2014 Quantum Inform. Proc. 13 1545 [22] Caraiman S and Manta V 2015 Quantum Inform. Proc. 14 1693 [23] Zhou R G, Wu Q, Zhang M Q and Shen C Y 2013 Int. J. Theor. Phys. 52 1802 [24] Zhou R G, Hu W and Fan P 2017 Quantum Inform. Proc. 16 212 [25] Parker J, Kenyon R V and Troxel D E 1983 IEEE Trans. Med. Imaging 2 31 [26] Zhou R G, Yang P L, Liu X A and Ian H 2018 Int. J. Quant. Inf. 16 1850060 [27] Zhou R G, Hu W, Fan P and Ian H 2017 Sci. Rep. 7 2511 [28] Le P Q, Iliyasu, A M, Dong F Y and Hirota K 2011 Theor. Comput. Sci. 412 1406 [29] Ruiz-Perez L and Garcia-Escartin J C 2017 Quantum Inform. Proc. 16 152 [30] Hafiz M Z, Begum Z, Islam M S and Rahman M M 2009 Inf. Technol. J. 8 208 [31] Li P and Liu X 2018 Int. J. Quantum Inform. 16 1850020 [32] Thapliyal H and Ranganathan N 2009 Proceedings of the IEEE Computer Society Annual Symposium on VLSI, Tampa pp. 229-234 [33] Thapliyal H and Ranganathan N 2011 2011 11th IEEE Conference on Nanotechnology (IEEE-NANO), IEEE pp. 1430-1435 [34] Muñoz-Coreas E and Thapliyal H 2019 IEEE Trans. Comput. 68 729 [35] Barenco A, Bennett C H, Cleve R, Divincenzo D P, Margolus N, Shor P, Sleator T, Smolin J A and Weinfurter H 1995 Phys. Rev. A 52 3457 [36] Nielsen M A and Chuang I L 2000 Quantum Computation and Quantum Information (Cambridge: Cambridge University Press) |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|