Skip to main content
Log in

EQIRHSI: enhanced quantum image representation using entanglement state encoding in the HSI color model

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

Image coding in quantum computing primarily focuses on encoding position and color information. This study introduces an enhanced quantum image representation by employing entanglement state encoding within the HSI color model. The proposed model employs a 2-qubit entanglement state to encode the H (hue) and S (saturation) channels. This approach significantly improves the efficiency of quantum image processing, including quantum image retrieval, in comparison with the QIRHSI representation model based on two superposition states. EQIRHSI, the enhanced model, paves the way for new avenues of research in quantum image processing, while retaining the advantages of the QIRHSI model. These advantages include superior intensity channel processing when compared to the MCQI model, as well as lower storage space requirements in contrast to the NCQI model. The paper delves into the polynomial preparation and complexity analysis for constructing EQIRHSI states, quantum image processing, and quantum image compression. Additionally, the study evaluates EQIRHSI’s performance by analyzing six images. The results demonstrate that EQIRHSI achieves an average compression rate of 83.91%, surpassing the MCQI model by 1.2 times.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

All relevant data are within the paper. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

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

    MATH  Google Scholar 

  2. Li, X., Fu, X., Yan, F., Zhong, Y., Lu, C., et al.: Current status and future development of quantum computation. Strateg. Study Chin. Acad. Eng. 24(4), 133–144 (2022)

    Google Scholar 

  3. Jaeger, G.: Classical and quantum computing. Quantum Information: An Overview, 203–217 (2007)

  4. Lisnichenko, M., Protasov, S.: Quantum image representation: a review. Quantum Mach. Intell. 5(1), 2 (2023)

    Article  Google Scholar 

  5. Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceeding of 35th Annual Symposium Foundations of Computer Science. IEEE Computer Soc. Press, Los Almitos, CA, pp. 124–134 (1994)

  6. Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, pp. 212–219 (1996)

  7. Yan, F., Iliyasu, A.M., Venegas-Andraca, S.E.: A survey of quantum image representations. Quantum Inf. Process. 15(1), 1–35 (2016)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  8. Barenco, A., Bennett, C.H., Cleve, R., DiVincenzo, D.P., Margolus, N., Shor, P., Weinfurter, H.: Elementary gates for quantum computation. Phys. Rev. A 52(5), 3457 (1995)

    Article  ADS  Google Scholar 

  9. Yan, F., Iliyasu, A.M., Le, P.Q.: Quantum image processing: a review of advances in its security technologies. Int. J. Quantum Inf. 15(03), 1730001 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  10. Li, H.S., Zhu, Q., Zhou, R.G., et al.: Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state. Quantum Inf. Process. 13(4), 991–1011 (2014)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  11. Le, P.Q., Dong, F.Y., 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  MathSciNet  MATH  Google Scholar 

  12. Jiang, N., Lu, X., Hu, H., et al.: A novel quantum image compression method based on JPEG. Int. J. Theor. Phys. 57(3), 611–636 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  13. Wang, J.Y., Song, X.H., El-Latif, A.A.A.: Efficient entropic security with joint compression and encryption approach based on compressed sensing with multiple chaotic systems. Entropy 24(7), 885 (2022)

    Article  ADS  MathSciNet  Google Scholar 

  14. Wang, S., Song, X.H., Niu, X.M.: A novel encryption algorithm for quantum images based on quantum wavelet transform and diffusion. In: Intelligent Data analysis and its Applications. Springer, Cham. Volume II, pp. 243–250 (2014)

  15. Song, X.H., Wang, S., Abd El-Latif, A.A.A., Niu, X.M.: Dynamic watermarking scheme for quantum images based on Hadamard transform. Multimed. Syst. 20, 379–388 (2014)

    Article  Google Scholar 

  16. Song, X.H., Wang, S., Niu, X.M., et al.: A dynamic watermarking scheme for quantum images using quantum wavelet transform. Quantum Inf. Process. 12(12), 3689–3706 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  17. Iliyasu, A.M., Le, P.Q., Dong, F., et al.: Watermarking and authentication of quantum images based on restricted geometric transformations. Inf. Sci. 186(1), 126–149 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hu, W.W., Zhou, R.G., El-Rafei, A., et al.: Quantum image watermarking algorithm based on Haar wavelet transform. IEEE Access 7, 121303–121320 (2019)

    Article  Google Scholar 

  19. Kong, F., Peng, Y.: Color image watermarking algorithm based on HSI color space. In: 2010 2nd International Conference on Industrial and Information Systems. IEEE, vol. 2, pp. 464–467 (2010)

  20. Wang, Z., Xu, M., Zhang, Y.: Review of quantum image processing. Arch. Comput. Methods Eng. 29(2), 737–761 (2022)

    Article  MathSciNet  Google Scholar 

  21. Venegas-Andraca, S., Bose, S.: Storing, processing, and retrieving an image using quantum mechanics. In: Proceedings of SPIE Conference of Quantum Information and Computation, vol. 5105, pp. 134–147 (2003)

  22. Latorre, J.I.: Image compression and entanglement. arXiv:quant-ph/0510031 (2005)

  23. Le, P., 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  MathSciNet  MATH  Google Scholar 

  24. Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  25. Sun, B., Iliyasu, A., Yan, F., Dong, F., Hirota, K.: An RGB multi-channel representation for images on quantum computers. J. Adv. Comput. Intell. Intell. Inf. 17(3), 404–417 (2013)

    Article  Google Scholar 

  26. Sang, J.Z., Wang, S., Li, Q.: A novel quantum representation of color digital images. Quantum Inf. Process. 16(2), 42 (2017)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  27. Sun, B., Le, P., Iliyasu, A., Yan, F., Garcia, J., Dong, F., Hirota, K.: A multi-channel representation for images on quantum computers using the RGB \(\alpha \) color space. In: IEEE 7th International Symposium on Intelligent Signal Processing (WISP), pp. 1–6 (2011)

  28. Zhang, Y., Lu, K., Gao, Y., Xu, K.: A novel quantum representation for log-polar images. Quantum Inf. Process. 12(9), 3103–3126 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  29. Li, H., Zhu, Q., Lan, S., Shen, C., Zhou, R., Mo, J.: Image storage, retrieval, compression and segmentation in a quantum system. Quantum Inf. Process. 12(6), 2269–2290 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  30. Li, H., Zhu, Q., Zhou, R., Song, L., Yang, X.: Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state. Quantum Inf. Process. 13(4), 991–1011 (2014)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  31. Yuan, S., Mao, X., Xue, Y., Chen, L., Xiong, Q., Compare, A.: SQR: a simple quantum representation of infrared images. Quantum Inf. Process. 13(6), 1353–1379 (2014)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  32. Jiang, N., Wang, J., Mu, Y.: Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio. Quantum Inf. Process. 14(11), 4001–4026 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  33. Khan, R.A.: An improved flexible representation of quantum images. Quantum Inf. Process. 18(7), 1–19 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  34. Loesdau, M., Chabrier, S., Gabillon, A.: Hue and Saturation in the RGB Color Space, pp. 203–212. In International conference on image and signal processing. Springer, Cham (2014)

    Google Scholar 

  35. Kamiyama, M., Taguchi, A.: HSI color space with same gamut of RGB color space. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100(1), 341–344 (2017)

    Article  ADS  Google Scholar 

  36. Kong, F., Peng, Y.: Color image watermarking algorithm based on HSI color space. In: 2010 2nd International Conference on Industrial and Information Systems. IEEE, vol. 2, pp. 464–467 (2010)

  37. Saravanan, G., Yamuna, G., Nandhini, S.: Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. In: 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp. 0462–0466 (2016)

  38. Taguchi, A., Hoshi, Y.: Color image enhancement in HSI color space without gamut problem. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 98(2), 792–795 (2015)

    Article  ADS  Google Scholar 

  39. Yan, F., Li, N., Hirota, K.: QHSL: a quantum hue, saturation, and lightness color model. Inf. Sci. 577, 196–213 (2021)

    Article  MathSciNet  Google Scholar 

  40. Chen, G.L., Song, X.H., Venegas-Andraca, S.E., et al.: QIRHSI: novel quantum image representation based on HSI color space model. Quantum Inf. Process. 21(1), 1–31 (2022)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  41. Su, J., Guo, X., Liu, C., Lu, S., et al.: An improved novel quantum image representation and its experimental test on IBM quantum experience. Sci. Rep. 11(1), 1–13 (2021)

    Article  ADS  Google Scholar 

  42. Su, J., Guo, X., Liu, C., et al.: A new trend of quantum image representations. IEEE Access 8, 214520–214537 (2020)

    Article  Google Scholar 

  43. Blotta, E., et al.: Enhancement of medical images in HSI color space. J. Phys. Conf. Ser. 332(1), 012041 (2011)

    Article  Google Scholar 

  44. Ghadirli, H.M., Nodehi, A., Enayatifar, R.: An overview of encryption algorithms in color images. Signal Process. 164, 163–185 (2019)

    Article  Google Scholar 

  45. Kartika, D.S.Y., Herumurti, D.: Koi fish classification based on HSV color space. In: 2016 International Conference on Information & Communication Technology and Systems (ICTS). IEEE (2016)

  46. Nakajima, N., Taguchi, A.: A novel color image processing scheme in HSI color space with negative image processing. In: 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE (2014)

  47. Yoshinari, K., Murahira, K., Hoshi, Y., et al.: Color image enhancement in improved HSI color space. In: 2013 International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, pp. 429–434 (2013)

  48. Le, P.Q., Dong, F.Y., 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  MathSciNet  MATH  Google Scholar 

  49. Wang, L., Ran, Q., Ma, J., et al.: QRCI: a new quantum representation model of color digital images. Opt. Commun. 438, 147–158 (2019)

    Article  ADS  Google Scholar 

  50. Ruan, Y., Chen, H., Liu, Z., et al.: Quantum image with high retrieval performance. Quantum Inf. Process. 15(2), 637–650 (2016)

    Article  ADS  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the Heilongjiang Provincial Natural Science Foundation (CN) (LH2022F032) and the Shandong Provincial Natural Science Foundation (CN) (ZR2022LLZ003). This work also was supported by the EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia. Also, Ahmed A. Abd El-Latif acknowledges the Talented Young Scientist Program: TYSP for their support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xianhua Song.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Song, X. & El-Latif, A.A.A. EQIRHSI: enhanced quantum image representation using entanglement state encoding in the HSI color model. Quantum Inf Process 22, 334 (2023). https://doi.org/10.1007/s11128-023-04092-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-023-04092-0

Keywords

Navigation