Skip to main content
SpringerLink
Log in

Joint encryption and compression of 3D images based on tensor compressive sensing with non-autonomous 3D chaotic system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Existing techniques for the simultaneous encryption and compression of three-dimensional (3D) image sequences (e.g., video sequences, medical image sequences) may come with sufficient decryption accuracy or compression ratio, but do not inherently have both; the relationship between them is generally ignored because the images of a sequence are handled individually. To address this problem, we designed Tensor Compressive Sensing (TCS) to simultaneously encrypt and compress a 3D sequence as a tensor rather than several 2D images. To further enhance security, a non-autonomous Lorenz system is constructed to control the three measurement matrices of TCS. The proposed method preserves the intrinsic structure of tensor-based 3D image sequences and achieves a favorable balance of compression ratio, decryption accuracy, and security. Numerical simulation results verify the validity and the reliability of the TCS scheme.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Alfalou A, Brosseau C, Abdallah N (2015) Simultaneous compression and encryption of color video images. Opt Commun 338:371–379

    Article  Google Scholar 

  2. Ballester RR, Suter SK, Pajarola R (2015) Analysis of tensor approximation for compression-domain volume visualization. Comput Graph 47:34–47

    Article  Google Scholar 

  3. Bernard N. S., Yousef S (2007) Higher Order Orthogonal Iteration of Tensors (HOOI) and its relation to PCA and GLRAM. Proceedings of the 7th SIAM International Conference on Data Mining, p 355–365

  4. Cambareri V, Marngia M, Pareschi F, Rovatti R, Setti G (2015a) On known-plaintext attacks to a compressed sensing-based encryption: a quantitative analysis. IEEE Trans Inf Forensic Secur 10(10):2182–2195

    Article  Google Scholar 

  5. Cambareri V, Mauro M, Fabio P et al (2015b) Low-complexity multiclass encryption by compressed sensing. IEEE Trans Signal Process 63(9):2183–2195

    MathSciNet  Google Scholar 

  6. Cesar FC, Andrzej C (2015) Stable, robust, and super fast reconstruction of tensors using multi-way projections. IEEE Trans Signal Process 63(3):780–793

    Article  MathSciNet  Google Scholar 

  7. Chai XL, Yang K, Gan ZH (2016) A new chaos-based image encryption algorithm with dynamic key selection mechanisms. Multimed Tools Appl. doi:10.1007/s11042-016-3585-x

    Google Scholar 

  8. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  9. Friedland S, Li Q, Schofeld D (2014) Compressive sensing of sparse tensors. IEEE Trans Image Process 23(10):4438–4446

    Article  MathSciNet  MATH  Google Scholar 

  10. Ji XY, Bai S, Zhu GB et al (2016) Image encryption and compression based on the generalized Knight’s tour, discrete cosine transform and chaotic maps. Multimedia Tools Appl. doi:10.1007/s11042-016-3684-8

    Google Scholar 

  11. Lang J, Zhang J (2015) Optical image cryptosystem using chaotic phase-amplitude masks encoding and least-data-driven decryption by compressive sensing. Opt Commun 338:45–53

    Article  Google Scholar 

  12. Lima JB, Madeiro F, Sales FJR (2015) Encryption of medical images based on the cosine number transform. Signal Process Image Commun 35:1–8

    Article  Google Scholar 

  13. Liu H, Xiao D, Liu YB et al (2015) Securely compressive sensing using double random phase encoding. Optik 126(2):2663–2670

    Article  Google Scholar 

  14. Liu XB, Mei WB, Du HQ (2016a) Simultaneous image compression, fusion and encryption algorithm based on compressive sensing and chaos. Opt Commun 366:22–32

    Article  Google Scholar 

  15. Liu Y, Tong XJ, Ma J (2016b) Image encryption algorithm based hyper-chaotic system and dynamic S-box. Multimed Tools Appl 75(13):7739–7759

    Article  Google Scholar 

  16. Marco FD, Richard GB (2012) Kronecker compressive sensing. IEEE Trans Image Process 21(2):494–504

    Article  MathSciNet  MATH  Google Scholar 

  17. Mishra B, Sepulchre R (2016) Riemannian preconditioning. SIAM J Optim 26(1):635–660

    Article  MathSciNet  MATH  Google Scholar 

  18. Mohamed FH, Gulliver TA (2015) A new 3D chaotic cipher for encrypting two data streams simultaneously. Nonlinear Dyn 81:1053–1066

    Article  Google Scholar 

  19. Muhammad R (2014) Color information verification system based on singular value decomposition in gyrator transform domains. Opt Lasers Eng 57:13–19

    Article  Google Scholar 

  20. Nirmala S, Aloka S (2015) Video encryption using chaotic masks in joint transform correlator. J Opt 17:1–8

    Google Scholar 

  21. Nitin R, Byoungho K, Rajesh K et al (2016) Fast digital image encryption based on compressive sensing using structurally random matrices and Arnold transform technique. Optik 127:2282–2286

    Article  Google Scholar 

  22. Qin Y, Wang ZP, Pan Q et al (2016) Optical color-image encryption in the diffractive-imaging scheme. Opt Lasers Eng 77:191–202

    Article  Google Scholar 

  23. Ran QW, Yuan L, Zhao TY (2015) Image encryption based on Nonseparable fractional Fourier transform and chaotic map. Opt Commun 348:43–49

    Article  Google Scholar 

  24. Rawat N, Hwang I, Shi Y, Lee BG (2015) Optical image encryption via photon-counting imaging and compressive sensing based Ptychography. J Opt 17(6):1–11

    Article  Google Scholar 

  25. Sandeep S, Sharma S, Thakur M et al (2016) Perceptual video hashing based on Tucker decomposition with application to indexing and retrieval of near-identical videos. Multimedia Tools Appl 75(13):7779–7797

    Article  Google Scholar 

  26. Sidiropoulos ND, Kyrillidis A (2012) Multi-way compressed sensing for sparse low-rank tensors. IEEE Signal Process Lett 19(11):757–760

    Article  Google Scholar 

  27. Tong XJ, Wang Z, Zhang M et al (2013) A new algorithm of the combination of image compression and encryption technology based on cross chaotic map. Nonlinear Dyn 72(1–2):229–241

    Article  MathSciNet  Google Scholar 

  28. Tong XJ, Zhang M, Wang Z (2016) A joint color image encryption and compression scheme based on hyper-chaotic system. Nonlinear Dyn. 84(4):2333–2356

    Article  Google Scholar 

  29. Yan CG, Zhang YD, Xu JZ et al (2014a) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  30. Yan CG, Zhang YD, Xu JZ et al (2014b) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circ Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  31. Yan CG, Zhang YD, Dai F et al (2014c) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):367–368

    Article  Google Scholar 

  32. Yan CG, Zhang YD, Dai F et al (2014d) Efficient parallel HEVC intra prediction on many-core processor. Electron Lett 50(11):805–806

  33. Yang HQ, Liao XF, Wong KW (2012) SPIHT-based joint image compression and encryption. Acta Phys Sin 61(4):29–36

    Google Scholar 

  34. Zeng WL, Du YJ, Hu CH (2016) Noise Supression by discontinuity indicator controlled non-local means method. Multimedia Tools Appl. doi:10.1007/s11042-016-3753-z

    Google Scholar 

  35. Zhang LB, Zhu ZL, Yang BQ et al (2015) Medical image encryption and compression scheme using compressive sensing and pixel swapping based permutation approach. Math Probl Eng 2015:1–9

    Google Scholar 

  36. Zhao SM, Wang L, Liang WQ, Cheng WW, Gong LY (2015) High performance optical encryption based computational ghost imaging with QR code and compressive sensing technique. Opt Commun 353:90–95

    Article  Google Scholar 

  37. Zhou NR, Li HL, Wang D (2015) Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform. Opt Commun 343:10–21

    Article  Google Scholar 

  38. Zhou NR, Pan SM, Cheng S et al (2016) Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82:121–133

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (61301257).

Author information

Authors and Affiliations

  1. School of Information Engineering, Northeast Dianli University, Jilin, 132012, China

    Qingzhu Wang, Mengying Wei & Xiaoming Chen

  2. Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, China

    Zhuang Miao

Authors
  1. Qingzhu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengying Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhuang Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingzhu Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Wei, M., Chen, X. et al. Joint encryption and compression of 3D images based on tensor compressive sensing with non-autonomous 3D chaotic system. Multimed Tools Appl 77, 1715–1734 (2018). https://doi.org/10.1007/s11042-017-4349-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4349-y

Keywords

Search

Navigation