Abstract
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.
Highlights
Images can provide rich information to human vision systems and have become one of the most important ways to transfer information
Yuen et al integrated a chaotic system, discrete cosine transform (DCT), the Secure Hash Algorithm-1 (SHA-1) and Huffman encoding for JICE, and the experiments confirmed that the presented scheme was efficient for both image compression and encryption [20]
Inspired by the extreme sensitivity of quantum chaos, compression ability of Compressive sensing (CS) and permutation power of Arnold, this paper proposes a novel approach that integrates a quantum chaotic system, sparse Bayesian learning and a bit-level 3D Arnold cat map, namely, QSBLA, for joint image compression and encryption
Summary
Images can provide rich information to human vision systems and have become one of the most important ways to transfer information. Yuen et al integrated a chaotic system, DCT, the Secure Hash Algorithm-1 (SHA-1) and Huffman encoding for JICE, and the experiments confirmed that the presented scheme was efficient for both image compression and encryption [20]. Tong et al proposed a JICE approach with high security, a good compression effect and high encryption speed by combining DWT and a cross-chaotic map [21]. Inspired by the extreme sensitivity of quantum chaos, compression ability of CS and permutation power of Arnold, this paper proposes a novel approach that integrates a quantum chaotic system, sparse Bayesian learning and a bit-level 3D Arnold cat map, namely, QSBLA, for joint image compression and encryption.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
