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Nonlinear Dynamics

, Volume 95, Issue 3, pp 2235–2261 | Cite as

Joint image compression–encryption scheme using entropy coding and compressive sensing

  • Yanjie Song
  • Zhiliang ZhuEmail author
  • Wei Zhang
  • Li Guo
  • Xue Yang
  • Hai Yu
Original Paper
  • 197 Downloads

Abstract

Recently, compressive sensing (CS)-based joint compression–encryption schemes have been widely investigated due to their high efficiency and good security for images. However, the existing schemes typically have a lower compression ratio (CR), and there may be a flaw during their compression processes. Therefore, in this paper, according to the intrinsic features of images, we propose a novel compression architecture to enhance the CR. Meanwhile, based on this architecture, a joint image compression–encryption scheme using entropy coding and CS is designed to implement a complete compression and encryption process. In this joint scheme, a presented bit-level lossless compression–encryption algorithm based on entropy coding for the higher bit-planes is incorporated to improve the quality of the reconstructed image and ensure the security. Alternately, this joint scheme also contains an improved CS-based lossy compression–encryption algorithm for the lower bit-planes, which can guarantee the efficiency and security. Through the cooperation between the proposed lossless and lossy coding, the higher reconstruction performance can be achieved. SHA-256 is combined with all initial keys in the proposed joint scheme to generate the updated keys for chaos cryptosystem to maintain high security and resist some common attacks. Experimental and analytical results illustrate the superiority of the proposed joint scheme compared with the existing compression–encryption schemes and JPEG, as well as good encryption performance.

Keywords

Joint image compression–encryption Compressive sensing Entropy coding Chaos Bit-plane 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 61374178, 61402092, 61603182), the Online Education Research Fund of the MOE Research Center for Online Education, China (Qtone Education, Grant No. 2016ZD306), the Ph.D. Start-Up Foundation of Liaoning Province, China (Grant No. 201501141), and the Fundamental Research Funds for the Central Universities (Grant No. N171704004).

Compliance with ethical standards

Conflict of interest

Compliance with ethical standards Conflict of interest. The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Software CollegeNortheastern UniversityShenyangChina
  2. 2.School of Computer Science and EngineeringNortheastern UniversityShenyangChina

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