Selective Image Encryption Based on DCT, Hybrid Shift Coding and Randomly Generated Secret Key

George, Loay E.; Hassan, Enas Kh.; Mohammed, Sajaa G.; Mohammed, Faisel G.

doi:10.24996/ijs.2020.61.4.25

Cited by 11 publications

(10 citation statements)

References 18 publications

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“…Delta Modulation (DM) DM is a special and simple form of differential pulse code modulation (DPCM), in which the difference between two adjacent samples is calculated to reduce the signal values using Eq. ( 5) and ( 6) [11]:…”

Section: Non-uniform Quantizationmentioning

confidence: 99%

“…Hashim & Ali [10], proposed another shift coding optimizer to find the optimal two code word sizes vital to represent the small and large sequence values in their compression system. George et al [11], encoded the data of a selective image encryption system using a proposed hybrid shift. Ibrahim et al [12], proposed an improved entropy encoder for a new high-performance lossy compression system.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study Using DCT, Delta Modulation, and Double Shift Coding for Compressing Electroencephalogram Data

Hadi

George

2022

eijs

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Storing, transferring, and processing high-dimensional electroencephalogram (EGG) signals is a critical challenge. The goal of EEG compression is to remove redundant data in EEG signals. Medical signals like EEG must be of high quality for medical diagnosis. This paper uses a compression system with near-zero Mean Squared Error (MSE) based on Discrete Cosine Transform (DCT) and double shift coding for fast and efficient EEG data compression. This paper investigates and compares the use or non-use of delta modulation, which is applied to the transformed and quantized input signal. Double shift coding is applied after mapping the output to positive as a final step. The system performance is tested using EEG data files from the CHB-MIT Scalp EEG Database. Compression Ratio (CR) is used to evaluate the compression system performance. The results are encouraging when compared with previous works on the same data samples.

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Section: Non-uniform Quantizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study Using DCT, Delta Modulation, and Double Shift Coding for Compressing Electroencephalogram Data

Hadi

George

2022

eijs

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“…Because processing the block in one-dimension manner is faster than when processed in two dimensions, and therefore accelerating the compression and decompression process. The resultant DCT transformed coefficients in each block is arranged in a way where the coefficient that holds the most significant information is called DC component and it resides in the upper left corner of the block, the rest of the coefficients holds the less important information in the block which is called AC [11]. 1D-DCT can be obtained by applying the equation 10to transform the images and reverse the transformation by applying the equation (11) to obtain the reconstructed image [12]:…”

Section: Horizontal Scanning Ordermentioning

confidence: 99%

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Ibrahim

George

Hassan

2020

eijs

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In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

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“…To obtain secure sensitive information, frequency transform methods like Discrete Cosine Transform (DCT), DWT based methods, machine learning and chaos theory can be used as encryption techniques [2,3]. Before further processing, it is constantly necessary to transform each value of every pixel into confirmed frequency components in the frequency domain encryption.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Encryption Based on Frequency Domain and Chaotic Map

Yousif¹,

Mahdi²,

Hashim³

2022

IJSSE

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Medical images typically have diagnostic and confidential data and information about the patient and are usually sent using public networks. Due to the sensitivity of medical images, their security has become a challenging requirement that must be addressed. Traditional cryptographical algorithms are inadequate to ensure appropriate and fine security while encrypting them, because of the correlation between each pixel, high redundancy of the image and its major size. Chaotic systems with their properties and partial encryption based on frequency domain are the best candidates for securing the storage and transfer of digital images. The paper shows new criteria for encryption of medical image. It is designed to improve performance and meet the increasing need for better security for medical image encryption. At first, the pixel's correlation is eliminated by scrambling the input image and diffusing them using secret sharing based on polynomials. Various frequency domains of the image are accomplished by applying the integer wavelet transform of the scrambled image, namely, the associated detail of (HL, LH and HH) and the LL (approximation coefficient) through using the AES algorithm. The (LL) part is encrypted to originate the diffusion image and by using the inverse of the Haar wavelet transform to produce a reliable, unbreakable and secure form. The designed algorithm is used to reverse and shuffle every frequency sign of the transformed image before transformation image back to the pixel domain. The original image form is restored through the reverse decryption algorithm. The suggested algorithm is measured and evaluated in a statistical way and normal standard security; The outcome of the proposed algorithm shows a strong resistant to the familiar attacks and extra secure than the other algorithms in the domain of image cryptography.

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Selective Image Encryption Based on DCT, Hybrid Shift Coding and Randomly Generated Secret Key

Cited by 11 publications

References 18 publications

A Comparative Study Using DCT, Delta Modulation, and Double Shift Coding for Compressing Electroencephalogram Data

A Comparative Study Using DCT, Delta Modulation, and Double Shift Coding for Compressing Electroencephalogram Data

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Medical Image Encryption Based on Frequency Domain and Chaotic Map

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