The image compression–encryption algorithm based on the compression sensing and fractional-order chaotic system

Ji, Xu; Mou, Jun; Liu, Jian; Jiang, Hao

doi:10.1007/s00371-021-02085-7

Cited by 37 publications

(11 citation statements)

References 43 publications

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“…AVM (x,y) = θ1Ab(x,y) + θ2As(x,y) + θ3Ae(x,y) (6) Yes Yes Yes [14] Yes Yes Yes [15] Yes [16] Yes Yes [17] Yes [18] Yes Yes [19] Yes Yes [20] Yes Yes Yes Yes Yes [21] Yes Yes [22] Yes [23] Yes Yes…”

Section: Multi-scale Structural Similarity Index (Ms-ssim)mentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.

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Section: Multi-scale Structural Similarity Index (Ms-ssim)mentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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“…(19): MSE=1M×N∑x=0M−1∑y=0N−1[f(x,y)r−f(x,y)p]2,where f(x,y)r is the reconstructed image and f(x,y)p is the plaintext image. While M and N are image dimensions 21 , 33 …”

Section: Experimental Analysis Of the Proposed Algorithmmentioning

confidence: 99%

Hybrid image encryption algorithm based on compressive sensing, gray wolf optimization, and chaos

Abdul-Kareem,

Mahmoud Al-Jawher

2023

J. Electron. Imag.

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Growing reliance on digital communications has necessitated development of dependable and secure technologies to ensure that the transmission and reception of images over the Internet do not pose a risk to the data of individuals and governments. We propose developing a hybrid image encryption and compression algorithm by combining compressive sensing, the gray wolf algorithm, and multidimensional chaotic systems. It aims to generate a highly secure encrypted image while conserving transmission and storage resources. This algorithm overlaps several stages designed to protect vital images while minimizing size. First, the image is converted to the frequency domain using the discrete wavelet transform. Then, the discrete wavelet transform coefficients are scrambled globally using the Waleed-Ali Map and the gray wolf algorithm. Second, the confused image is measured by a parameters-controlled matrix to reduce transmission costs. The final encrypted image is obtained after performing the diffusion operation with a bitstream derived from the Nahrain chaotic map. The average peak signal-to-noise ratio score was 53.1995, and the average mean squared error score was 0.6130, demonstrating that the plaintext and decrypted images are identical. The average correlation coefficient score was −0.010095; the average entropy analysis was 7.9987; and the average number of pixel change rate and unified average changing intensity analyses were 99.60 and 33.52, respectively. The experimental results demonstrate the algorithm's efficiency and robustness, as well as the high quality of the reconstructed image.

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“…Synchronization of the FOC systems is a challenging task that has attracted great attention due to its potential applications in various fields of science and engineering such as coupled laser systems in nonlinear optics and secure communication (Li and Wu, 2019; Sayed and Radwan, 2020; Xu et al, 2022; Yang et al, 2020), power converters, and chemical reactions (Mofid et al, 2019). In the literature, several effective methods have been introduced to achieve chaos synchronization such as sliding mode control (Razzaghian et al, 2021), active control (Bagheri and Ozgoli, 2016; Tang, 2014), backstepping control (Shukla and Sharma, 2017), linear matrix inequality (LMI) technique (Pourgholi and Boroujeni, 2016), and fuzzy approach (Chen et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Synchronization of time-varying fractional-order chaotic systems in the presence of unknown disturbance using a fast disturbance identification

Gharamaleki

Badamchizadeh

Ghaemi

et al. 2023

Transactions of the Institute of Measurement and Control

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This paper studies the synchronization of fractional-order chaotic systems in the presence of unknown external disturbances. A new disturbance identification method using fractional-order algebraic identification is proposed to handle unknown disturbances. In order to guarantee system stability, the nonlinear state-feedback controller is designed based on the Lyapunov theory. All system parameters and orders, as well as the external disturbances, are considered time-varying in this paper. The simulation results show that the estimation of unknown disturbance is executed more quickly and accurately using the proposed method, which also leads to fast and precise synchronization of the chaotic systems. A circuit implementation is also performed to identify the unknown disturbances in a fractional-order electronic chaotic oscillator system to evaluate the practicability of the proposed approach.

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The image compression–encryption algorithm based on the compression sensing and fractional-order chaotic system

Cited by 37 publications

References 43 publications

Survey On Parameters of Data Compression

Survey On Parameters of Data Compression

Hybrid image encryption algorithm based on compressive sensing, gray wolf optimization, and chaos

Synchronization of time-varying fractional-order chaotic systems in the presence of unknown disturbance using a fast disturbance identification

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