PSNR vs SSIM: imperceptibility quality assessment for image steganography

Setiadi, De Rosal Ignatius Moses

doi:10.1007/s11042-020-10035-z

“…Where is an encrypted image, is the original image, is the image width, is the image height, and is the number of layers, is the image covariance against ; 2 is a variant of image ; 2 is a variant of image ; 1 = ( 1 ) 2 ; 2 = ( 2 ) 2 ; is a dynamic range of the image(0 − 255) with the default value 1 = 0.01 and 2 = 0.03 [32].…”

Section: Resultsmentioning

confidence: 99%

Improved Color Image Encryption using Hybrid Modulus Substitution Cipher and Chaotic Method

Harjo¹,

Setiadi²

2021

IJIES

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This study proposes the hybrid substitution cryptography method followed by chaotic methods to improve the security of digital image transactions on the internet. The substitution method used is a hybrid of the modification of the Vigenère and Beaufort cipher algorithm. To do a hybrid, two random keys are used. The first random key is a matrix with 8-bits integer values, while the second random key is a matrix that contains binary values. The modulus operation is used in the main process of substitution methods. In this study, 24-bits true-color images were used as a dataset for the testing experiment. Measurement of encryption quality is measured by Entropy, MSE, PSNR, SSIM, UACI, and NPCR, whereas tic toc functions for measuring the computational time needed. To ensure the decryption process runs perfectly, measuring instruments such as SSIM, MSE, and PSNR are used to compare the decrypted image with the original image, the tic toc function is also used to measure computing performance. Based on the results of testing the quality of image encryption proved to have excellent results where the entropy value is very close to 8, the ideal NPCR and UACI values, as well as excellent visual values based on MSE, PSNR, and SSIM measurements. The image can also be decrypted perfectly with relatively fast computing time in the encryption and decryption process.

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“…the parallel CS ADMM and PGD algorithms and the parallel, over-sampled least squares approach, dSparse, at various precision using floating point (FP), fixed point (FXP) and unum posit (UP) arithmetic. In all cases, full, double precision floating point computation using dSparse is considered as the baseline comparison for the metrics for quality of reconstruction, i.e PSNR and SSIM [45]. For the CS Fig.…”

Section: Accuracy Of Reconstructionmentioning

confidence: 99%

Energy Efficient Approximate 3D Image Reconstruction

Wu

¹

,

Aßmann

²

,

Stewart

³

et al. 2022

IEEE Trans. Emerg. Topics Comput.

3

0

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We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (CS) and approximate computing. Employing data parallelism for rapid image formation, the depth image is reconstructed from sparsely sampled scenes using convex optimization. Coupled with faster imaging, this sparse sampling reduces significantly the projected laser power in active systems such as LiDAR to allow eye safe operation at longer range. We also demonstrate how reduced precision is leveraged to reduce the number of logic units in FPGA implementations for such sparse imaging systems. It enables significant reduction in logic units, memory requirements and power consumption by over 80% with minimal impact on the quality of reconstruction. To further accelerate processing, pre-computed, important components of the lower-upper (LU) decomposition and other linear algebraic computations are used to solve the convex optimization problems. Our methodology is demonstrated by the application of the alternating direction method of multipliers (ADMM) and proximal gradient descent (PGD) algorithms. For comparison, a fully discrete least square reconstruction method (dSparse) is also presented. This demonstrates the feasibility of novel, high resolution, low power and high frame rate LiDAR depth imagers based on sparse illumination for use in applications where resources are strictly limited.

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“…SSIM Analysis. In this experiment, we aim to evaluate our approach based on the structural index similarity (SSIM) metric, which is a tool used to measure image quality [49]. Especially in the image steganography, SSIM and PSNR are used to measure the quality of imperceptibility.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Especially in the image steganography, SSIM and PSNR are used to measure the quality of imperceptibility. SSIM is designed based on brightness, contrast, and structure to better suit the performance of the human visual system [49]. SSIM can be defined as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

A New Method of Coding for Steganography Based on LSB Matching Revisited

Fateh

¹

,

Rezvani

²

,

Irani

³

2021

Security and Communication Networks

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LSB matching revisited is an LSB-based approach for image steganography. This method is a type of coding to increase the capacity of steganography. In this method, two bits of the secret message are hidden in two pixels with only one change. But this method provides no idea for hiding a message with a large number of bits. In other words, this method works only for n = 2 , where n is the number of bits in a block of the secret message. In this paper, we propose an improved version of the LSB matching revisited approach, which works for n > 2 . The proposed scheme contains two phases including embedding and extracting the message. In the embedding phase, we first convert the secret message into a bit-stream, and then the bit-stream is divided into a set of blocks including n bits in each block. Then we choose 2 n − 1 pixels for hiding such n bits of the secret message. In the next step, we choose the operations needed to generate such a message. Finally, we perform the obtained operations over the coefficients to hide the secret message. The proposed approach needs fewer changes than LSB MR when n > 2 . The capacity of the proposed approach is 2 n − 1 / 2 n − 1 − 1 × 100 % higher than the F5 method where this value for n > 2 is bigger than 75%. For example, the capacity of our scheme is 75% higher than the capacity of F5 for n = 3 . The proposed method can be used in the first step of every steganography method to reduce the change in the stego image. Therefore, this method is a new coding method for steganography. Our experimental results using steganalysis show that using our method provides around 10% higher detection error for SRNet over two steganography schemes.

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PSNR vs SSIM: imperceptibility quality assessment for image steganography

Cited by 412 publications

References 43 publications

Improved Color Image Encryption using Hybrid Modulus Substitution Cipher and Chaotic Method

Improved Color Image Encryption using Hybrid Modulus Substitution Cipher and Chaotic Method

Energy Efficient Approximate 3D Image Reconstruction

A New Method of Coding for Steganography Based on LSB Matching Revisited

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