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Image compression and encryption algorithm based on compressive sensing and nonlinear diffusion

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Abstract

Compressive sensing is widely used to image compression and encryption algorithms due to its high efficiency, but the existing algorithms have some flaws and insufficiency such as low reconstruction quality, small key space and weak security. Therefore, in this paper, a novel 5D chaotic system is proposed, which has larger key space and more complex key stream. According to the proposed 5D chaotic system, an image compression and encryption algorithm based on compressive sensing and nonlinear diffusion is proposed. In addition, in order to improve the image reconstruction quality of compressive sensing, an algorithm is proposed in this paper to optimize the measurement matrix of compressive sensing. Theoretical analysis shows that the proposed 5D chaotic system is chaotic and it shows many superior properties. The algorithm proposed to optimize the measurement matrix is also proved effective for reconstruction quality. The simulation results show that our algorithm has advantages in compression performance, key sensitivity, key space and time complexity, and it can also resist statistical attack and other common attacks.

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Acknowledgements

This work was supported by the following projects and foundations: the National Natural Science Foundation of China (No.61902091), project ZR2019MF054 supported by Shandong Provincial Natural Science Foundation and the Fundamental Research Funds for the Central Universities (HIT.NSRIF.2020099), the Foundation of Science and Technology on Information Assurance Laboratory (No.KJ-17-004), Equip Pre-research Projects of 2018 supported by Foundation of China Academy of Space Technology (No. WT-TXYY/ WLZDFHJY003), 2017 Weihai University Co-construction Project.

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Correspondence to Xiaojun Tong.

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Liu, J., Zhang, M., Tong, X. et al. Image compression and encryption algorithm based on compressive sensing and nonlinear diffusion. Multimed Tools Appl 80, 25433–25452 (2021). https://doi.org/10.1007/s11042-021-10884-2

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  • DOI: https://doi.org/10.1007/s11042-021-10884-2

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