Color Image Compression-Encryption Using Fractional-Order Hyperchaotic System and DNA Coding

Abstract

Recently, many encryption algorithms based on fractional-order chaotic system have been proposed to solve the problem of image encryption. In this paper, we propose a novel color image compression-encryption algorithm based on fractional-order hyperchaotic system and DNA coding. In the data compression stage, the data of R, G, and B channels of the color image are converted to the frequency domain by two-dimensional discrete cosine transform (DCT), and then the amount of encrypted data is reduced by the quantization process. We design a data processing algorithm to ensure that the data after DCT is compatible with DNA coding data format. In the encryption stage, the processes of DNA encoding and decoding, DNA operation, and pixel scrambling are all controlled by the corresponding chaotic sequences, which are generated by the chaotic system. The original image is used to calculate the initial state of the chaotic system, which improves the performance of the algorithm against the chosen-plaintext attack significantly. Experimental results and security analysis illustrate that the proposed algorithm has excellent compression and security performance. It can not only reconstruct the original image well under the condition of low compression ratio, but also provide high security to resist various attacks. Besides, experimental results also indicate that the algorithm proposed can be applied to the fields of color image compression, encryption, and transmission. INDEX TERMS Color image encryption, DNA encoding, fractional-order hyperchaotic system, discrete cosine transform(DCT), image compression.

Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia

“…e multiresolution transformation characteristics of wavelet transform enable the digital multimedia video image to make full use of the visual characteristics of human eyes and retain the detail information of the original image under various resolutions. Suppose that h and g, respectively, represent the low-pass filter and high pass filter of the same wavelet base corresponding to the multimedia video image; C j−1 represents the original digital multimedia video image; C j represents the brightness value of the digital multimedia video image after wavelet transform; d 1 j , d 2 j , and d 3 j represent the edge subimage of digital multimedia video image after wavelet transform [18].…”

“…where in, the first item of equation (18) represents that the low-frequency component of the digital multimedia video image is smooth in all directions; the second item represents the smoothing of the low-frequency component of the image in the same direction [22]. Which is mainly used to capture the edge information and contour information of multimedia video multiresolution image.…”

Multi-Image Compression–Encryption Algorithm Based on Compressed Sensing and Optical Encryption

“…Obviously, it can be seen from Table 4 and Table 5 that the information entropy of the R component, G component and B component of the color ciphertext image exceeds 7.99, which is better than the information entropy of the reference [ 26 ]. The information entropy of the grayscale ciphertext image is also around eight.…”

A gray image encryption algorithm based on 3D chaotic map and DNA operations

“…Considering only the above equation, the exchange between pixel points (1, j) and (1, q) can be realized through pseudo-random variables a and b. At the same time, ab+1 is regarded as a new random number denoted by a, then Equation (2) becomes as shown in Equation (3).…”

“…This makes the encryption technology based on text image difficult to meet the needs of image encryption. Therefore, how to ensure that these private images are not attacked and stolen in the transmission process has become a research hotspot [2,3]. This makes more and more cryptographers keen on designing a secure and effective image encryption method.…”