scholarly journals Image Watermarking Based on IWT and Parity Bit Checking

2021 ◽  
pp. 2726-2739
Author(s):  
Jalal H. Awad ◽  
Balsam D. Majeed
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Floating Point ◽  
Integer Coefficient ◽  
Authentication Mechanism ◽  
Image Pixels

     Various document types play an influential role in a lot of our lives activities today; hence preserving their integrity is an important matter. Such documents have various forms, including texts, videos, sounds, and images.  The latter types' authentication will be our concern here in this paper. Images can be handled spatially by doing the proper modification directly on their pixel values or spectrally through conducting some adjustments to some of the addressed coefficients. Due to spectral (frequency) domain flexibility in handling data, the domain coefficients are utilized for the watermark embedding purpose. The integer wavelet transform (IWT), which is a wavelet transform based on the lifting scheme, is adopted in this paper in order to provide a direct way for converting image pixels' integer values to integer coefficient values rather than floating point coefficients that could be produced by the traditional wavelet transform. This direct relation can enhance the processed image quality due to avoiding the rounding operations on the floating point coefficients. The well-known parity bit approach is also utilized in this paper as an authentication mechanism, where 3 secret parity bits are used for each block in an image which is divided into non-overlapped blocks in order to enforce a form of fragile watermark approach. Thus, any alteration in the block pixels could cause the adopted (even) parity to be violated. The fragile watermarking is achieved through the modification of least significant bits ((LSBs) of certain frequency coefficients' according to the even parity condition. In spite of this image watermarking operation, the proposed method is efficient. In order to prove the efficiency of our proposed method, it was tested against standard images using measurements like peak signal to noise ratio (PSNR) and structural similarity index (SSIM).  Experiments showed promising results; the method preserves high image quality (PSNR≈ 44.4367dB, SSIM≈ 0.9956) and good tamper detection capability.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals New model for local fractional integral of Chebyshev polynomials for image denoising

2019 ◽  
pp. 22-28
Author(s):  
Suzan J Obaiys ◽  
Hamid A Jalab ◽  
Rabha W Ibrahim
Keyword(s):  
Image Denoising ◽  
Chebyshev Polynomials ◽  
Fractional Integral ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Input Image ◽  
Convolution Product ◽  
Local Fractional Integral ◽  
Image Pixels

The use of local fractional calculus has increased in different applications of image processing. This study proposes a new algorithm for image denoising to remove Gaussian noise in digital images. The proposed algorithm is based on local fractional integral of Chebyshev polynomials. The proposed structures of the local fractional windows are obtained by four masks created for x and y directions. On four directions, a convolution product of the input image pixels with the local fractional mask window has been performed. The visual perception and peak signal-to-noise ratio (PSNR) with the structural similarity index (SSIM) are used as image quality measurements. The experiments proved that the accomplished filtering results are better than the Gaussian filter. Keywords: local fractional; Chebyshev polynomials; Image denoising

scholarly journals Lifting Wavelet Transform in Compressive Sensing for MRI Reconstruction

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Indrarini Dyah Irawati ◽  
Sugondo Hadiyoso ◽  
Gelar Budiman ◽  
Asep Mulyana
Keyword(s):  
Wavelet Transform ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Lifting Wavelet Transform ◽  
Structural Similarity Index ◽  
Compressed Sampling ◽  
Lifting Wavelet

Compressed sampling in the application of magnetic resonance imaging compression requires high accuracy when reconstructing from a small number of samples. Sparsity in magnetic resonance images is a fundamental requirement in compressed sampling. In this paper, we proposed the lifting wavelet transform sparsity technique by taking wavelet coefficients on the low pass sub-band that contains meaningful information. The application of novel methods useful for compressing data with the highest compression ratio at the sender but still maintaining high accuracy at the receiver. These wavelet coefficient values are arranged to form a sparse vector. We explore the performance of the proposed method by testing at several levels of lifting wavelet transform decomposition, include Levels 2, 3, 4, 5, and 6. The second requirement for compressed sampling is the acquisition technique. The data sampled sparse vectors using a normal distributed random measurement matrix. This matrix is normalized to the average energy of the image pixel block. The last compressed sampling requirement is a reconstruction algorithm. In this study, we analyze three reconstruction algorithms, namely Level 1 magic, iteratively reweighted least squares, and orthogonal matching pursuit, based on structural similarity index measured and peak signal to noise ratio metrics. Experimental results show that magnetic resonance imaging can be reconstructed with higher structural similarity index measured and peak signal to noise ratio using the lifting wavelet transform sparsity technique at a minimum decomposition level of 4. The proposed lifting wavelet transforms and Level 1 magic reconstruction algorithm has the best performance compared to the others at the measurement rate range between 10 to 70. This method also outperforms the techniques in previous studies.

scholarly journals Dual tree complex wavelet transform based image denoising for Kalpana satellite images

2018 ◽  
Vol 7 (3.29) ◽  
pp. 269
Author(s):  
Naga Lingamaiah Kurva ◽  
S Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Pictorial Representation ◽  
Complex Wavelet Transform ◽  
Raw Data ◽  
Complex Wavelet ◽  
Kalpana Satellite

This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.   

scholarly journals Medical Image Watermarking Technique Using Image Interpolation in Transform Domain

2018 ◽  
Vol 7 (3.12) ◽  
pp. 711
Author(s):  
S Priya ◽  
B Santhi ◽  
J Raja Mohan
Keyword(s):  
Medical Image ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Medical Data ◽  
Experimental Result ◽  
Structural Similarity Index ◽  
Medical Image Watermarking

In telemedicine, medical data are shared across the world among different specialists for various purposes through an unsecured medium. So there is a need to protect the medical data during transmission. With the help of image watermarking techniques, medical images are protected along with the electronic patient information (EPI). This paper proposes a medical image watermarking, by applying wavelet transform, using an interpolation technique. EPI data is embedded within the transformed medical image to generate a watermarked image. At the extraction side, EPI data are extracted and medical image is reconstructed without any loss. The performance of the proposed method is analyzed using a peak signal to noise ratio (PSNR), mean absolute error (MAE) and structural similarity index (SSIM).   The experimental result shows that the proposed method gives better results.

scholarly journals Chimera: A New Efficient Transform for High Quality Lossy Image Compression

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 378
Author(s):  
Walaa Khalaf ◽  
Ahmad Saeed Mohammad ◽  
Dhafer Zaghar
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Suggested Approach ◽  
Simple Slope ◽  
Image Pixels ◽  
Karhunen Loeve Transform ◽  
The Cost ◽  
Index Measure

A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of 2.0272 for DCT, 1.179 for WT and 4.301 for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of 0.1108 for DCT, 0.051 for WT and 0.175 for KLT.

scholarly journals CONTENT PRESERVING WATERMARKING FOR MEDICAL IMAGES USING SHEARLET TRANSFORM AND SVD

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 101-108 ◽  
Author(s):  
M. N. Favorskaya ◽  
E. I. Savchina
Keyword(s):  
Medical Image ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Main Idea ◽  
Structural Similarity ◽  
Mathematical Framework ◽  
Dicom Standard ◽  
Shearlet Transform ◽  
Types Of Information

Medical Image Watermarking (MIW) is a special field of a watermarking due to the requirements of the Digital Imaging and COmmunications in Medicine (DICOM) standard since 1993. All 20 parts of the DICOM standard are revised periodically. The main idea of the MIW is to embed various types of information including the doctor’s digital signature, fragile watermark, electronic patient record, and main watermark in a view of region of interest for the doctor into the host medical image. These four types of information are represented in different forms; some of them are encrypted according to the DICOM requirements. However, all types of information ought to be resulted into the generalized binary stream for embedding. The generalized binary stream may have a huge volume. Therefore, not all watermarking methods can be applied successfully. Recently, the digital shearlet transform had been introduced as a rigorous mathematical framework for the geometric representation of multi-dimensional data. Some modifications of the shearlet transform, particularly the non-subsampled shearlet transform, can be associated to a multi-resolution analysis that provides a fully shift-invariant, multi-scale, and multi-directional expansion. During experiments, a quality of the extracted watermarks under the JPEG compression and typical internet attacks was estimated using several metrics, including the peak signal to noise ratio, structural similarity index measure, and bit error rate.

scholarly journals JSCC-Cast: A Joint Source Channel Coding Video Encoding and Transmission System with Limited Digital Metadata

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6208
Author(s):  
Jose Balsa ◽  
Óscar Fresnedo ◽  
José A. García-Naya ◽  
Tomás Domínguez-Bolaño ◽  
Luis Castedo
Keyword(s):  
Image Quality ◽  
Channel Coding ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Perceived Quality ◽  
Transmission System ◽  
Video Encoding ◽  
Practical Implementation ◽  
Temporal Redundancy

This work considers the design and practical implementation of JSCC-Cast, a comprehensive analog video encoding and transmission system requiring a reduced amount of digital metadata. Suitable applications for JSCC-Cast are multicast transmissions over time-varying channels and Internet of Things wireless connectivity of end devices having severe constraints on their computational capabilities. The proposed system exhibits a similar image quality compared to existing analog and hybrid encoding alternatives such as Softcast. Its design is based on the use of linear transforms that exploit the spatial and temporal redundancy and the analog encoding of the transformed coefficients with different protection levels depending on their relevance. JSCC-Cast is compared to Softcast, which is considered the benchmark for analog and hybrid video coding, and with an all-digital H.265-based encoder. The results show that, depending on the scenario and considering image quality metrics such as the structural similarity index measure, the peak signal-to-noise ratio, and the perceived quality of the video, JSCC-Cast exhibits a performance close to that of Softcast but with less metadata and not requiring a feedback channel in order to track channel variations. Moreover, in some circumstances, the JSCC-Cast obtains a perceived quality for the frames comparable to those displayed by the digital one.

GPU Based Image Quality Assessment using Structural Similarity (SSIM) Index

2016 ◽  
pp. 276-282
Author(s):  
Mahesh Satish Khadtare
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Video Processing ◽  
Image Quality Assessment ◽  
Assessment Tool ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
University Of Texas ◽  
Computationally Intensive

This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.

scholarly journals Compressed-Sensing Magnetic Resonance Image Reconstruction Using an Iterative Convolutional Neural Network Approach

2020 ◽  
Vol 10 (6) ◽  
pp. 1902
Author(s):  
Fumio Hashimoto ◽  
Kibo Ote ◽  
Takenori Oida ◽  
Atsushi Teramoto ◽  
Yasuomi Ouchi
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Similarity Index ◽  
Structural Similarity ◽  
Neural Network Approach ◽  
Priori Information ◽  
Space Data

Convolutional neural networks (CNNs) demonstrate excellent performance when employed to reconstruct the images obtained by compressed-sensing magnetic resonance imaging (CS-MRI). Our study aimed to enhance image quality by developing a novel iterative reconstruction approach that utilizes image-based CNNs and k-space correction to preserve original k-space data. In the proposed method, CNNs represent a priori information concerning image spaces. First, the CNNs are trained to map zero-filling images onto corresponding full-sampled images. Then, they recover the zero-filled part of the k-space data. Subsequently, k-space corrections, which involve the replacement of unfilled regions by original k-space data, are implemented to preserve the original k-space data. The above-mentioned processes are used iteratively. The performance of the proposed method was validated using a T2-weighted brain-image dataset, and experiments were conducted with several sampling masks. Finally, the proposed method was compared with other noniterative approaches to demonstrate its effectiveness. The aliasing artifacts in the reconstructed images obtained using the proposed approach were reduced compared to those using other state-of-the-art techniques. In addition, the quantitative results obtained in the form of the peak signal-to-noise ratio and structural similarity index demonstrated the effectiveness of the proposed method. The proposed CS-MRI method enhanced MR image quality with high-throughput examinations.

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