Novel quantum image compression and encryption algorithm based on DQWT and 3D hyper-chaotic Henon map
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Novel quantum image compression and encryption algorithm based on DQWT and 3D hyper-chaotic Henon map Nan-Run Zhou1
· Lang-Xin Huang1 · Li-Hua Gong1,2 · Qing-Wei Zeng3
Received: 6 November 2019 / Accepted: 2 April 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract A novel quantum image compression and encryption algorithm with Daubechies D (4) quantum wavelet transform (DQWT) and 3D hyper-chaotic Henon map is presented. The quantum image is firstly scrambled by the iterative generalized Arnold transforms to eliminate its block effect. Then, the produced quantum image is compressed with DQWT and measurement matrix, which could be implemented with Hadamard gate. Subsequently, a quantum key image is constructed by a hyper-chaotic Henon sequence generated by 3D hyper-chaotic Henon map under the control of three initial values and two parameters. The quantum key image is XORed with the produced quantum compression image. The key space is relatively large enough since there are three initial values and two parameters involved. Numerical simulations demonstrate that the proposed quantum image compression and encryption algorithm is feasible, secure and efficient. Keywords 3D hyper-chaotic Henon map · Iterative generalized Arnold transform · Quantum image compression and encryption · Quantum cryptography
1 Introduction The continuous development of quantum computing has been laying a theoretical foundation for the development of quantum information processing . Quantum image
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Nan-Run Zhou [email protected] Qing-Wei Zeng [email protected]
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Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
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Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh 15261, USA
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Department of Computer Science and Engineering, Nanchang University, Nanchang 330031, China 0123456789().: V,-vol
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processing, as a branch of quantum information processing, has naturally evolved. As we all know, a quantum register with n qubits can store 2n information data, which implies the needed storage space of image information can be exponentially reduced with quantum register. Therefore, more and more researchers engaged themselves to research quantum image encryption algorithm. Meanwhile, the representation model of quantum image is constantly being advanced. A model to store, process and retrieve a quantum image with quantum mechanics was designed [1]. Latorre cast the pixel values of an image into a real ket of a Hilbert space with an appropriate block structured addressing [2]. Venegas-Andraca devised a novel method for storing and retrieving binary geometrical shapes in line with quantum entanglement properties [3]. Subsequently, flexible representation of quantum images (FRQI) [4], novel enhanced quantum representation (NEQR) [5], generalized quantum image representation (GQIR) were successively proposed [6]. Based on these proposed quantum image representation models, many useful and efficien
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