Multifractal measures of image quality

In this work an Artificial Neural Network with Radial Basis Function (RBF) is employed to model a predictor, utilized in Differential Pulse Code Modulation (DPCM) scheme. The RBF predictor estimates the magnitude of signal incoming to DPCM, the error between incoming signal and estimated one is applied to quantiser unit. The resultant error contains a few bit word length and is transmitted in data format towards a receiver unit. The output of the RBF predictor is added to error at the receiver part of DPCM to produce actual output. In this study application of RBF predictor in DPCM is explained. The potential offered by DPCM scheme using RBF predictor leads to a considerable amount of reduction in word length of filter coefficients.

Aim: High quality of all image elements must be maintained for the purpose of telepathology. The aim of this study was to compress the images of electrophoresis and isoelectric focusing samples using JPEG (Joint Photographic Expert Group) and JPEG2000 algorithms, and to assess the quality of the compressed images before and after electronic transmission. Methods: Scanograms of serum protein electrophoresis samples of a patient with Zagreb albumin and albumin samples for isoelectric focusing of a patient with Krapina albumin were selected for the study together with a photographed scanogram of isoelectric focusing. Each image was compressed at eight compression rates (from 3.00 bpp (bit per pixel) to 0.1 bpp) using JPEG and JPEG2000 compression algorithms. All images (N = 51), both compressed and uncompressed, we retransmitted by email for assessment to eight medical biochemists: six from Croatia, one from Italy and one from Denmark. Image quality was also assessed by objective measures, i.e. compared to the quality of PSNR (Peak-Signal-to-Noise-Ratio), SNR (Signal-to-Noise-Ratio), OQF (Optimized Quality Factor) and MSE (Mean Squared Error) images. Results: All images compressed using the JPEG2000 algorithm were subjectively rated as excellent. Contrarily, images compressed using JPEG at 0.1 bpp were rated as completely useless, those at 0.2 bpp as moderately blurred, and those at 0.3-3.00 as excellent. At JPEG compression at 0.3 bpp, PSNR and SNR values corresponded to PSNR and SNR values obtained by JPEG2000 compression at 0.1 bpp.

Digital images contain large amount of information that need evolving effective techniques for storing and transmitting the ever increasing volumes of data. Image compression addresses the problem by reducing the amount of data required to represent a digital image. Discrete Cosine Transformation (DCT), quantization and entropy encoding are the steps in the compression of the JPEG image format. In this proposed work, the performance analysis of JPEG is carried out. Code is written for the Joint Photographic Experts Group (JPEG), the factors Compression Ratio (CR), Signal to Noise Ratio (SNR), Peak SNR (PSNR) and Mean Square Error (MSE) are studied considering different compression ratio for different images. It is found that performance will not remain same for different images even though compression factor was same.

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

One of the container media that is available and popular is the Joint Photographic Experts Group (JPEG) format image. This article aims to determine the effect of Quality Factor on the secret message capacity of JPEG image steganography and stego image quality. The quality of an image can actually be seen subjectively with the human eye, but this is relative between each individual. Because the assessment of the human eye varies from person to person. In addition, the effect of Quality Factor on secret message capacity is not yet known whether it has an impact. Therefore, in this study the Quality Factor is used to objectively see the secret message capacity of the JPEG image steganography and the quality of the stego image. The parameter used to determine the quality of an image is the Peak Signal to Noise Ratio (PSNR). PSNR will compare the quality of the original image (before steganography) with the stego image. The test results show that the Q Factor effect can affect the secret message capacity of the JPEG image steganography and the stego image quality. The bigger the Q Factor, the more the message capacity is generated. The greater the Q factor, the better the quality of the resulting stego image.

Wavelet is one method that can be used as a step to recognition an individual . Wavelet is used to perform an image feature extraction . In the process of image texture analysis , showed the value of the coefficients of a wavelet . The magnitude of the coefficients obtained using wavelet feature extraction is influenced by several factors . Besides affected by the type of wavelet used , is also influenced by the magnitude of the wavelet decomposition level of itself. In the wavelet is known as the wavelet energy retention , which means the number of retained energy after undergoing a process of decomposition and cutting coefficients . During the decomposition process , the calculation for texture analysis is often a constraint . In order for the current calculation lighter texture analysis , necessary to the process of cutting coefficients based on the retention factor of the wavelet coefficients . Based on these issues , created a program to analyze the influence of variations in the level of wavelet decomposition and coefficient of variation coefficient of cutting a cut on the image . The object of this final project is 30 iris image that has been converted into polar form presented . Having experienced the process of cutting coefficients , to prevent the image of the reverse transformation has a big difference to the original image , in this study was calculated Mean Square Error ( MSE), Peak Signal to Noise Ratio ( PSNR), and Euclidean distance to determine the level of similarity of image input and output images . The Highest values of Retention ​, MSE and the Euclidean distance is obtained at the level of decomposition of 1 and the lowest at the level of decomposition 6 . While the truncated coefficient and PSNR values ​​obtained at the highest level of decomposition of 6 and the lowest level of decomposition of 6 . The variation coefficient of pieces , value retention and highest PSNR obtained at koefsien piece 5 and the lowest coefficient of 50 pieces . While the coefficient value is truncated , MSE and the highest Euclidean distance is obtained at koefsien pieces 50 and the lowest coefficient of 5 pieces . Keywords: iris, texture analysis, Haar wavelet transform, MSE, PSNR, E uclidean distance

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

In recent years, internet revolution resulted in an explosive growth in multimedia applications. The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. Besides this, it is easier to modify and misuse the valuable information through hacking at the same time. Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form, image or text that is impressed onto paper, which provides evidence of its authenticity. In this paper an invisible watermarking technique (least significant bit) and a visible watermarking technique is implemented. This paper presents the general overview of image watermarking and different security issues. Various attacks are also performed on watermarked images and their impact on quality of images is also studied. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the message/logo into the image. This work has been implemented through MATLAB. Keywords - Watermarking, Least Significant Bit (LSB), JPEG (Joint Photographic Experts Group), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

This paper analyzes the quality of the six most significant operators to detect the edges through the parameters of the Peak Signal to Noise Ratio (PSNR), signal to noise ratio (SNR), Mean Square Error (MSE) and Entropy over dental images. The analysis results are presented graphically and in tables. Model analysis was performed through the conversion of primary images in the pictures with different edge detection, and analysis of images obtained through the above parameters and varying degrees of bits per pixel (bpp). The analysis shows that operators Pyramid and Roberts give much better results at lower levels of bpp, whilst, according to Entropy of image, best results are being given by Pyramid operator. The study is consisted of two parts: first analyzing the parameters PSNR, SNR and MSE through bpp and afterwards analyzing Entropy images and level of detail.

The field of telemedicine has gained immense momentum, owing to the need for transmitting patients’ information securely. This paper puts forth a unique method for embedding data in medical images. It is based on edge based embedding and XOR coding. The algorithm proposes a novel key generation technique by utilizing the design of a sudoku puzzle to enhance the security of the transmitted message. The edge blocks of the cover image alone, are utilized to embed the payloads. The least significant bit of the pixel values are changed by XOR coding depending on the data to be embedded and the key generated. Hence the distortion in the stego image is minimized and the information is retrieved accurately. Data is embedded in the RGB planes of the cover image, thus increasing its embedding capacity. Several measures including peak signal noise ratio (PSNR), mean square error (MSE), universal image quality index (UIQI) and correlation coefficient (R) are the image quality measures that have been used to analyze the quality of the stego image. It is evident from the results that the proposed technique outperforms the former methodologies.

There are many uses of an image quality measure. It is often used to evaluate the effectiveness of an image processing algorithm, yet there is no one widely used objective measure. In many papers, the mean squared error (MSE) or peak signal to noise ratio (PSNR) are used. Though these measures are well understood and easy to implement, they do not correlate well with perceived image quality. This paper will present an image quality metric that analyzes image structure rather than entirely on pixels. It extracts image structure with the use of quadtree decomposition. A similarity comparison function based on contrast, luminance, and structure will be presented.

Video Steganography provides a secure way of communication by embedding the secret message inside video frames. Supported by a real-time environment, a significant growth in choosing video as a cover file has been noticed in the last decade. Large concealing capacity, more robustness to attacks, and indistinguishability of cover video and stego video are of major concern for any video steganography technique. In this paper, a novel technique has been proposed to achieve the above-mentioned objectives. Implementation of video steganography in the transform domain inherently contributes to improved robustness. We have embedded an image inside a chosen video file using the Lifting Scheme based on the Haar Wavelet Transform. To achieve increased concealing capacity, the Singular Value Decomposition technique has been applied before the actual embedding task. The proposed work is implemented using the MATLAB environment. The suggested method is then tested and analyzed for its robustness, capacity, and imperceptibility. It is concluded that proposed technique is robust against general video processing attacks, like low-pass filtering, scaling, rotation, transformation, and histogram equalization attacks. Additionally, the values for Peak Signal to Noise Ratio, Mean Square Error, Structure Similarity Index Matrix, and Correlation Coefficients were calculated and analyzed as a performance measure of the method.

A prequantizer with the human visual effect for the DPCM

In recent years, there has been increased interest in the use of evolutionary algorithms (EAs) in the design of robust image transforms for use in defense and security applications. An EA replaces the defining filter coeffcients of a discrete wavelet transform (DWT) to provide improved image quality within bandwidth-limited image processing applications, such as the transmission of surveillance data by swarms of unmanned aerial vehicles (UAVs) over shared communication channels. The evolvability of image transform filters depends upon the properties of the underlying fitness landscape traversed by the evolutionary algorithm. The landscape topography determines the ease with which an optimization algorithm may identify highly-fit filters. The properties of a fitness landscape depend upon a chosen evaluation function defined over the space of possible solutions. Evaluation functions appropriate for image filter evolution include mean squared error (MSE), the universal image quality index (UQI), peak signal-to-noise ratio (PSNR), and average absolute pixel error (AAPE). We conduct a theoretical comparison of these image quality measures using random walks through fitness landscapes defined over each evaluation function. This analysis allows us to compare the relative evolvability of the various potential image quality measures by examining fitness topology for each measure in terms of ruggedness and deceptiveness. A theoretical understanding of the topology of fitness landscapes aids in the design of evolutionary algorithms capable of identifying near-optimal image transforms suitable for deployment in defense and security applications of image processing.