SSIM

A Reversible Watermarking (RW) using Difference Expansion (DE) algorithm plays an important role on content authentication for highly secured medical and military images. According to this algorithm, the least significant bit (LSB) of inter-pixel differences (between a pair of neighboring pixel) is used to embed secret data. It is seen that none of the DE works focuses on structural information retentions for the watermarked image at high embedding capacity. Moreover, security measure of the hidden data is not investigated under distortion constraint scenario. To this aim, a modification in DE is proposed that not only increase embedding space but also makes little change in structure and contrast comparison under similar luminance background. In our approach an image is partitioned into edge, smooth and texture region using computed gradient magnitude obtained through sobel’s operator followed by adaptive distortion control. Simulation results shown that, this simple yet effective modification leads to better security of the hidden data.

Image quality assessment is a process of undoing or recovering an image from degraded stage. Knowledge of degradation is needed for successful restoration. The image restoration techniques are used to make the corrupted image as compatible to the original image. Hyper spectral image are alter or corrupted by the mixture of diverse kinds of noise in the acquisition process, which may include stripes, impulse noise, Gaussian noise, deadlines, , and so on. This paper introduces the new image quality assessment methods for the quality semantic gap evaluation for restoration of the image data, which can simultaneously removes the Gaussian noise, impulse noise, deadlines, and stripes. But there is no spatial constraint applied on neighboring pixels that originates large areas of missing pixels. To handle the issue of missing pixels non-reference regularization algorithm has been proposed. The proposed model is projected to improve the overall quality of the image quality assessment methods by correctly analyzing the quality of the image matrix.

Medical Imaging is playing the key role in diagnosing and treatment of diseases such as locating the tumours in brain, thin fractures in bones, detection of cancer cells in early stages etc. For making accurate decisions the images acquired by various medical imaging modalities must be free from noise. So Image denoising became an important pre-processing step in Medical image analysis. Developing the denoising algorithms is a difficult task because diagnostic information must be preserved while removing the noise. Earlier the denoising algorithms were designed in the spatial domain such as median filtering, harmonic filtering, and weiner filtering etc. by directly working on the pixel values, these methods will remove noise while introducing the blur in the denoised images. At present advanced mathematical models such as Partial differential equations which are useful for edge preservation and multiresolution analysis useful for preserving directional oriented information and texture became very popular in developing the denoising techniques. In this paper we are using Total Variational approach (PDE method) and Complex Dual Tree wavelet transform (Multiresolution analysis)method to denoise the medical images and we perform the fusion of the two denoised images resulting from the above denoising techniques. The performance of the proposed algorithm is compared with the existing methods using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index.). The performance of the fusion process is measured using MI (Mutual Information), Edge Association and Spatial Frequency (SF) measures. The results showed that the proposed method is having better PSNR values diagnostically acceptable and very much useful for the diagnosis& treatment phases.

Histogram Equalization is a simple and effective contrast enhancement technique. In spite of its popularity Histogram Equalization still have some limitations –produces artifacts, unnatural images and the local details are not considered, therefore due to these limitations many other Equalization techniques have been derived from it with some up gradation. In this proposed method statistics play an important role in image processing, where statistical operations is applied to the image to get the desired result such as manipulation of brightness and contrast. Thus, a novel algorithm using statistical operations and neighborhood processing has been proposed in this paper where the algorithm has proven to be effective in contrast enhancement based on the theory and experiment.

Ultrasonograms refer to images generated through ultrasonography, a technique that applies ultrasound pulses to delineate internal structures of the body. Despite being useful in medicine, ultrasonograms usually suffer from multiplicative noises that may limit doctors to analyse and interpret them. Attempts to address the challenge have been made from previous works, but denoising ultrasonograms while preserving semantic features remains an open-ended problem. In this work, we have proposed a diffusion-steered model that gives an effective interplay between total variation and Perona-Malik models. Two parameters have been introduced into the framework to convexify our energy functional. Also, to deal with multiplicative noise, we have incorporated a log-based prior into the framework. Empirical results show that the proposed method generates sharper and detailed images. Even more importantly, our framework can be evolved over a longer time without smudging critical image features.

This paper presents various types of interpolation techniques to obtain a high quality image The difference between the proposed algorithm and conventional algorithms (in estimation of missing pixel value) is that if standard deviation of image is used to calculate pixel value rather than the value of nearmost neighbor, the image gives the better result. The proposed method demonstrated higher performances in terms of PSNR and SSIM when compared to the conventional interpolation algorithms mentioned.

Image denoising is the process to remove the noise from the image which contains noise. Wavelet transform technique is a unique mathematical manipulation framework used for medical image denoising and enhancement implementation. The wavelet techniques are effective to remove the noise due to their ability to capture the energy of a signal in a few energy transform values. A dual-tree complex wavelet transform is used to present the image enhancement process. The proposed technique has the cascaded structure of DTCWT used for generation of different frequency bands for analysis. In this paper, a denoising approach based on dual-tree complex wavelet and Wiener filter technique is used. The result has a better balance between smoothness and accuracy than the DWT and is less redundant than SWT. We used the SIM (Structural Similarity Index Measure) along with PSNR (Peak Signal to Noise Ratio) to assess the quality of denoised images.

An edge is sharp change in intensity of an image But, since the overall goal is to locate edges in the real world via an image, the term edge detection is commonly used. An edge is not a physical entity, just like a shadow. It is where the picture ends and  the  wall  starts,  where  the  vertical and the  horizontal surfaces  of  an  object  meet.  If there  were  sensor  with  infinitely  small footprints and zero-width point spread functions, an edge would be recorded between pixels within in an image. In reality, what appears to be an edge from the distance may even contain other edges when looked close-up. The edge between a forest and a road in an aerial photo may not look like an edge any more in a image taken on the ground.In our proposed algorithm we have taken an image and then we have apply our proposed algorithm.

In this paper, we present a novel extension technique to the Set Partitioning in Hierarchical Trees (SPIHT) based image compression with spatial scalability. The present modification and the preprocessing techniques provide significantly better quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity. There are two proposals for this paper. Firstly, we propose a pre-processing scheme, called Zero-Shifting, that brings the spatial values in signed integer range without changing the dynamic ranges, so that the transformed coefficient calculation becomes more consistent. For that reason, we have to modify the initialization step of the SPIHT algorithms. The experiments demonstrate a significant improvement in visual quality and faster encoding and decoding than the original one. Secondly, we incorporate the idea to facilitate resolution scalable decoding (not incorporated in original SPIHT) by rearranging the order of the...

This paper is an extension of work originally presented at conference Applied Machine Intelligence and Informatics (SAMI), 2017 IEEE 13th International Symposium. The article performs a comparative study between the image compression technique Local Binary Compressed format (LBC) proposed by the authors and the standard image compression techniques used today (BMP, JPEG, PNG and GIF). The study is carried out for large medical images that need to be stored for longer periods of time. In the study, the most common types of medical images used are: X-rays and ultrasounds images. This study aimed to determine the distribution laws parameters used to reconstruct the image as well as to determine the influence of the most used medical image formats on the compression process. The effective representation of image format LBC is proven using an image quality index type, the Structural Similarity Index (SSIM).

Ultrasonograms refer to images generated through ultrasonography, a technique that applies ultrasound pulses to delineate internal structures of the body. Despite being useful in medicine, ultrasonograms usually suffer from multiplicative noises that may limit doctors to analyse and interpret them. Attempts to address the challenge have been made from previous works, but denoising ultrasonograms while preserving semantic features remains an open-ended problem. In this work, we have proposed a diffusion-steered model that gives an effective interplay between total variation and Perona-Malik models. Two parameters have been introduced into the framework to convexify our energy functional. Also, to deal with multiplicative noise, we have incorporated a log-based prior into the framework. Empirical results show that the proposed method generates sharper and detailed images. Even more importantly, our framework can be evolved over a longer time without smudging critical image features.

Currently, digital terrain models (DTMs) are important constituent of any urban navigation system. When modeling the earth's surface in digital form, there occurs voids or gaps in the terrain data. This may result in an erroneous representation of the geographic information system. Terrain fusion is suggested as a solution to fill the voids in the data representation. This paper focuses on this idea and presents a novel terrain fusion methodology and evaluation using satellite imagery (SRTM) and 2D map representation (NDM). We use iso-elevation contour map, a compact feature based representation to evaluate the reference terrain (SRTM) and the fused terrain (SRTM and NDM). We experiment with these criterions of aerial difference, structural similarity and height deviation to estimate the accuracy of fusion methodology. The result shows that the proposed methodology can improve the accuracy in the terrain matching.

In this paper, we present a novel extension technique to the Set Partitioning in Hierarchical Trees (SPIHT) based image compression with spatial scalability. The present modification and the preprocessing techniques provide significantly better quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity. There are two proposals for this paper. Firstly, we propose a pre-processing scheme, called Zero-Shifting, that brings the spatial values in signed integer range without changing the dynamic ranges, so that the transformed coefficient calculation becomes more consistent. For that reason, we have to modify the initialization step of the SPIHT algorithms. The experiments demonstrate a significant improvement in visual quality and faster encoding and decoding than the original one. Secondly, we incorporate the idea to facilitate resolution scalable decoding (not incorporated in original SPIHT) by rearranging the order of the...

Abstract-With the increasing variety of mobile applications, reducing the energy utilization of mobile devices is a major challenge in multimedia streaming applications. In this paper we investigate energy-efficient offloading policy for transcoding as a service in a generic mobile cloud system. we explores how to minimize the energy consumption of the backlight when displaying a video stream without adversely impacting the user’s visual experience. we put forward algorithms to solve the fundamental problem and prove the optimality in terms of energy savings. Finally, based on the algorithms, we present a cloud-based energy-saving service. The proposed offloading policy can reduce energy consumption on both mobile devices and the cloud jointly, which provides guidelines for the design of green mobile cloud.

Este trabalho aborda a reducao no espaco de armazenamento de imagens utilizando a Decomposicao em Valores Singulares (SVD) de uma matriz. O metodo da decomposicao SVD e aplicado em imagens RGB (Cores Reais), HSV, com Cores Indexadas e de Intensidade em tons de cinza. Nos ensaios, as imagens sao reconstruidas com quantias distintas de valores singulares. Para simplificar a analisee empregado uma medida de qualidade SSIM (Structural SIMilarity) em cada imagem processada.

This paper mainly aims to study the performance of objective assessment methods of image quality. It take into consideration the correlations between each objective assessment and the subjective assessment in order to determine objective test performance. Three objective assessment methods used in this study are the Structural Similarity (SSIM) index, the Peak Signal-to-Noise Ratio (PSNR) and the Mean Squared Error (MSE) calculating algorithm. The resulting data indicate what type of objective assessment was most suitable for which type of impairment imposed upon an image. This is clarified using the Pearson Correlation Coefficient as described in the paper. As an overall, SSIM index had the best correlation characteristics to the subjective assessment, followed by the MSE calculating algorithm. From this study, a better understanding of the requirements for developing an efficient image quality assessment method was gained.

Sustainable development has been environmentally adopted into a development concept implemented in regulations/ legislations in accordance with the fourth amendment to the 1945 Constitution of the Republic of Indonesia (UUD 1945). Sustainable development is influenced by three aspects, namely: social aspects; economic aspects; and environmental aspects. Conventional development takes into account the influence of social and economic aspects, so in this study reviewing the influence of environmental aspects in the era of nowadays sustainable development. The purpose of this study is to analyse the elements of environmental aspects in form of a Model of Structural Hierarchy (MoSH) that af ects sustainable development. Environmental aspects elements are analysed by the Interpretive Structural Modelling (ISM) method. The ISM method was chosen to help decision makers in determining policies in accordance with the resulting a MoSH. Thus, the MoSH in the field of environment which is associated with sustainable development is a new contribution in the area of sustainable development.