Joint image compression and encryption based on sparse Bayesian learning and bit-level 3D Arnold cat maps


Autoři: Xinsheng Li aff001;  Taiyong Li aff002;  Jiang Wu aff002;  Zhilong Xie aff002;  Jiayi Shi aff002
Působiště autorů: College of Computer Science, Sichuan University, China aff001;  School of Economic Information Engineering, Southwestern University of Finance and Economics, China aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224382

Souhrn

Image compression and image encryption are two essential tasks in image processing. The former aims to reduce the cost for storage or transmission of images while the latter aims to change the positions or values of pixels to protect image content. Nowadays, an increasing number of researchers are focusing on the combination of these two tasks. In this paper, we propose a novel joint image compression and encryption approach that integrates a quantum chaotic system, sparse Bayesian learning (SBL) and a bit-level 3D Arnold cat map, so-called QSBLA, for such a purpose. Specifically, the QSBLA consists of 6 stages. First, a quantum chaotic system is employed to generate chaotic sequences for subsequent compression and encryption. Second, as one method of compressive sensing, SBL is used to compress images. Third, an operation of diffusion is performed on the compressed image. Fourth, the compressed and diffused image is transformed into several bit-level cubes. Fifth, 3D Arnold cat maps are used to permute each bit-level cube. Finally, all the bit-level cubes are integrated and transformed into a 2D pixel-level image, resulting in the compressed and encrypted image. Extensive experiments on 8 publicly-accessed images demonstrate that the proposed QSBLA is superior or comparable to some state-of-the-art approaches in terms of several measurement indices, indicating that the QSBLA is promising for joint image compression and encryption.

Klíčová slova:

Algorithms – Baboons – Compressed sensing – Cryptography – Image processing – Imaging techniques – Permutation – Chaotic systems


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Článek vyšel v časopise

PLOS One


2019 Číslo 11