Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

Chaudhary, Pratibha; Gupta, Ritu; Singh, Abhilasha; Majumder, Pramathesh; Pandey, Ayushi

doi:10.1016/j.procs.2020.03.218

Cited by 22 publications

(6 citation statements)

References 6 publications

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“…Block division of images has been done for three modes with dimensions 16, 32, and 64. Figure (4) shows the process view of the proposed method. In this article, we have used two meta-heuristic algorithms, one based on evolutionary structure (genetics) and the other based on collective intelligence (gray wolf) as test steps.…”

Section: -Suggested Methodsmentioning

confidence: 99%

“…Since the transfer of images between communication paths is a costly process, the main goal in image compression is to reduce the number of bits needed to display an image [3]. As we know, the identification of data redundancy is the most important part in image compression, and among the types of data redundancy, we can mention cryptographic redundancy [4], inter-pixel redundancy [5] and psychooptical redundancy [6]. The main goal of image compression algorithms is to try to reduce all types of image redundancies, but which type of algorithm to use depends on how to use the information in the image.…”

Section: Introductionmentioning

confidence: 99%

“…Figure(4) The process of compressing view and calculating recognition accuracy in the proposed method…”

mentioning

confidence: 99%

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WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

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One of the important research areas in imaging is the formation of images, which plays an important role in many different applications, including surveillance, control, and security affairs. On the other hand, high spatial resolution is one of the most important factors for increasing image quality, but it increases the amount of storage memory. Based on this, it is expected that the processing methods will be concentrated in this area. In face recognition systems, one of the existing challenges is maintaining the image recognition rate. These images in the recognition system may affect the efficiency of the system, and as a result, it may be inferred that the percentage of recognition is found. Proposing a method that at least does not reduce detection rates would be very desirable. This article investigates how to compress facial images with high spatial resolution using innovative algorithms to reduce or even increase their accuracy as much as possible. In this article, meta-heuristic algorithms (genetics and gray wolf) are used in a way that they are responsible for identifying the important and similar areas of matching macroblocks in the whole image segmentation. So how arranging the length of the bit string of each block is appropriate to achieve the target estimation to achieve the highest recognition accuracy value, PSNR value, and SSIM value of the image set? In the simulation and evaluation section, the facial images of the CIE and FEI databases have been examined as a selective study, and the recognition efficiency of the images for the conditions without/with compression and the common SPIHT and JPEG coding methods and compared with the proposed method. The simulation results show the significant impact of the proposed methods using meta-heuristic algorithms in increasing the quality of PSNR and SSIM in contrast to the recognition efficiency. According to the proposed method, the larger the value of dividing the blocks, the better the average PSNR and SSIM, assuming the highest recognition accuracy can be reached in both meta-heuristic algorithms. In general, depending on the type of application of the problem, there is a compromise to achieve the highest average PSNR or SSIM, using a genetic algorithm or gray wolf. The gray wolf algorithm, however, reaches its optimal answer much faster than the genetic algorithm.

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Section: -Suggested Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

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“…Security evaluation index of image encryption algorithm: after image encryption, it is first necessary to ensure that the encrypted image shows a seemingly random and meaningless picture to the outside world, and that the third party can't get any effective information through intuitive feeling, and that all kinds of cryptographic analysis methods still can't get any plaintext information, that is, the security of the cryptographic system should be strong; At the same time, because of the large amount of image data, intelligent vehicles require high real-time performance of algorithms, so whether fast encryption/decryption can be realized in the case of large amount of data is also an important index to measure the system [11][12][13] .…”

Section: Design Of Multi-chaos Fast Image Encryption Algorithmmentioning

confidence: 99%

Research on network security detection based on image encryption algorithm

Yao

2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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“…The primary objective of image compression is to reduce the number of bits necessary to display an image, since the transfer of images is a costly process [3]. Image compression begins with identifying data redundancy, and among the types of data redundancy, we can mention cryptographic redundancy [4], inter-pixel redundancy [5], and psychovisual redundancy [6]. The main objective of image compression algorithms is to reduce all types of image redundancy.…”

Section: Introductionmentioning

confidence: 99%

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

khodadadi

Ardeshir

Grailu

2023

IJE

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In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.

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Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

Cited by 22 publications

References 6 publications

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

Research on network security detection based on image encryption algorithm

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

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