Informatics Publishing Limited

Yogesh Rathore ¹, Manish K. Ahirwar ¹, Rajeev Pandey ¹

Affiliations
1 University Institute of Technology, RGPV, Bhopal, M.P., IN

Abstract

Data Compression is the science of representing data in a compact form. For last decades, Data compression has been one of the critical enabling technologies for the ongoing digital data revolution. There are lot of data compression algorithms which are available to compress files of different formats. This paper present survey of several lossless data compression techniques and its corresponding algorithms. A set of selected algorithms are studied and examined. This paper concluded by stating which algorithm performs well for text data.

Keywords

Compression, Encoding, HUFFMAN, HLZ, LZ, LZW, RLE and RLL.

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Deepak Rao Khadatkar ¹, Yogesh Rathore ¹

Affiliations
1 Raipur Institute of Technology, Raipur, (Chhattisgarh), IN

Abstract

Analysis of medical images is often intricate and time intense, even for experienced physicians. The aid of image scrutiny and machine learning can make this process easier.. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier recognition and treatment stages, in which the time issue is very vital to determine the infection in the patient as possible as fast, especially in various cancer tumors such as the lung cancer, breast cancer. Lung cancer images passed basic three stages to achieve more quality and accuracy in our experimental results, firstly image enhancement stage which is low pre-processing image techniques. Gabor filter, using a Gaussian rule in which produced the preeminent resultant enhanced images, In the image segmentation stage, thresholding segmentation mechanism by marker using the gradient magnitude as the segmentation function and computed the watershed renovate of the segmentation function. Finally features which assist to make a contrast between normal and abnormal images were. Two features computed were: black and white pixels percentage of the input image and the second feature is image Masking and tagging.

Keywords

Lung Cancer, Enhancement, Segmentation.

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Yogesh Rathore ¹, Avinash Dhole ¹, Ram Nivas Giri ¹

Affiliations
1 Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur (C.G.) 492101, IN

Abstract

In this paper, we are proposing a fully automatic method for adding colors to grayscale images. In contrast to many previous computer-aided colorizing methods, which require intensive and accurate human intervention, this method needs only the user to provide a target gray level image for the process of 'colorization', a colorful image of the similar content as the grayscale image is automatically retrieved from the database of images, as an input source image. Then, the source and target image are both transformed into a perceptually de-correlated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Once a best matching source pixel is found, its chromaticity values are assigned (transferred) to the target pixel while the original luminance value of the target pixel is retained. The only requirement of the method is that the compositions of the source and target scenes resemble each other.

Keywords

Image Coloring, Image Retrieval, Color Transfer, Luminance Matching.

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Umesh Agrawal ¹, Yogesh Rathore ¹, Achin Tripathi ¹

Affiliations
1 National Informatics Center Services Inc., Raipur (C.G.), IN

Abstract

In this paper, we are proposing an algorithm for a content-based method for retrieving images. In contrast to many previous computer-aided methods, which require intensive and accurate human intervention, this method needs only the user to provide a image database of the similar content as the query image.

For input, the method requires a true color source image database and a target image. The source and target image are both transformed into a perceptually de-correlated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Along with the luminance distribution, high pass filter is used toimprove texture information in L-channel of the source and target imagesand edge detector is used in order to detect the edges of the source and target images. The only requirement of the method is that the compositions of the source and target scenes resemble each other.

Keywords

Luminance Matching, Content Based Image Searching, Histogram, High Pass Filter.

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Deepak Kumar Dewangan ¹, Yogesh Rathore ¹

Affiliations
1 Raipur Institute of Technology, Raipur (CG), IN

Abstract

As the requirement for Image Quality Evaluation is needed in many application areas. Image quality review is one of the challenging fields of digital image processing system. Measurement of visual quality is of fundamental importance for abundant image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. The evaluation of image quality based on single strategy Human Vision System (HVS) may not very sufficient. We need some more dimensions. Full Reference method. Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Average Difference (AD), Normalized Absolute Error (NAE), Structural Content (SC) and Maximum Difference (MD) may contribute to calculate efficient result to image quality measurements.

Keywords

Full Reference, Quality Assessment.

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Preeti Agrawal ¹, Yogesh Rathore ¹

Affiliations
1 Raipur Institute of Technology, Raipur, IN

Abstract

In High Performance Computing (HPC), providing adequate resources for user applications is crucial. For instance, a computing center that a user has access to cannot handle the user applications with short deadlines due to limited computing infrastructure in the center [2]. Therefore, to get the application completed by the deadline, users usually try to get access to several computing centers (resources). However, managing several resources, potentially with different architectures, is difficult for users. Another difficulty is optimally scheduling applications in such environment. In this paper we are giving the strategy how the resource managed in cloud environment.

Keywords

Cloud Computing, HPC, Resource.

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