Rajiv Kumar ¹, A. M. Arthanariee ²

Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore (Tamil Nadu), IN
2 Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, IN

Abstract

In this paper, a novel approach of K-Region based Clustering image segmentation algorithm has been proposed. The proposed algorithm divides an image of size N × N into K number of regions. The K and N are multiples of 2. The value of K must be less than N. Authors divided the image into 4, 16, 64, 256, 1024, 4096 and 16384 regions, based on the value of K. The adjacent pixels having similar intensity value in each region are grouped into same clusters. Further, the clusters of similar values in each adjacent region are grouped together to form the bigger clusters. The different segmented images have been obtained based on the K number of regions. The four parameters, namely, Probabilistic Rand Index (PRI), Variation of Information (VOI), Global Consistency Error (GCE) and Boundary Displacement Error (BDE) have been used to evaluate the performance of the proposed algorithm. The performance of proposed algorithm was evaluated using 100 images taken from Berkeley image database. The time-complexity of the proposed algorithm has also been calculated. The comparative analysis of proposed algorithm was made with existing image segmentation algorithm, namely, K-mean clustering and Region-growing algorithm. Significant results were obtained in case of proposed algorithm when\the PRI, VOI, GCE and BDE values were compared with those of existing algorithms. MATLAB 7.4 has been used to implement the proposed algorithm.

Keywords

Image Segmentation, Clusters, Regions, K-mean Clustering, Region-growing, MATLAB 7.4

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Ifra Bilal ¹, Rajiv Kumar ¹

Affiliations
1 Department of Computer Science and Engineering, Sharda University, Greater Noida - 201306, Uttar Pradesh, IN

Abstract

Large demand of internet services requires our data to be transmitted in a secure manner. Data can be transmitted secretly using three security methods: Steganography, cryptography and watermarking. Among these, steganography provides better confidentiality. It is the art and science of hiding information in ways that prevents the detection of the hidden message. We focus in this paper on audio steganography where the carrier to hold the secret image is a digital audio file. The novelty of this paper lies on the implementation of QR decomposition technique in audio steganography. To the best of our knowledge no QR technique has been implemented in audio steganography. The paper embeds the bits of the secret image in the lower diagonal elements of the carrier audio file using QR decomposition technique. Three different formats have been specified for input secret image: .jpg, .bmp, .png. The paper also embeds the secret image along the frequency distribution of the carrier audio using Fast Fourier Transform. Another contribution of this paper is comparison between Fast Fourier Transform and QR decomposition technique. The quality of the image is measured using PSNR. The result shows that QR decomposition technique is better than existing Fast Fourier Transform as the calculated PSNR is above 40 dB for all the input images. Further, the paper compares the results obtained from QR technique with other existing methods like SVD. It can be noted that the accuracy of the QR decomposition technique is more than the existing methods and has proven to be very efficient. The system can be used for covert communication or tamper proofing.

Keywords

Audio Signal, Confidentiality,Fast Fourier Transform, Steganography, QR Decomposition Technique

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Sushil Kumar ¹, Rajiv Kumar ¹

Affiliations
1 Department of CS&E, Sharda University, Greater Noida − 201306, Uttar Pradesh, IN

Abstract

Objectives: The use of Function Point Analysis for Multimedia software estimation may lead to wrong estimates and incomplete tasks, so there is a need to introduce a new method which estimates Multimedia software and Web Application. Methods/Statistical Analysis: The calculation of Multimedia Points (MP) is depended on the five factor of the software application. The factors are screen design, animation, graphics capabilities, audio and video; and further categorized as low, medium, or high. On the basis of this factor’s complexity, Unadjusted Multimedia Points (UMP) is calculated. Then the calculation of multimedia point advances via an adjustment level, in which the cost adjustment factor for multimedia (MCAF) is, depended on a collection of General System Characteristic for Multimedia (MGSC). These MGSC are rated according to the weight age of each characteristic for a software application, this range is known as level of impact and it varies from 0 to 5. Multimedia Point is computed as MP = UMP*MCAF. Findings: Comparison of outcomes derived from the estimation and through the practical observation for the E-Learning Application - Class XII – Physics is that on an average this project takes an approximately four man days to produce one hour of content using the defined platform. It was observed that approximately 110 hours content of the application, developer spent approximately 3520 man hours to produce it. This data has been taken from the documented report of the Company from their budgetary allocation file. In the experiment, the MPs calculated approximately 110 x 04 hours of content for class XII, Physics is 344 MPs., which translates 344 x 11= 3784 hours of the estimated effort spent in the research project. The experimental outcomes derived from the estimation and through the practical observation for the E-Learning application which have already been validated and compared with the documented report. It was found to have comparable effort spent vis-a-vis estimated MPs within margin of error being in range of ± 7%. Application/Improvements: Factors Considered as mixed media objects leads to complex counting. The scope of improvements will emphasize on how to use the prototype counting program, which impacts on production development environment or maintenance phase for complex system architecture.

Keywords

Cost Adjustment Factor for Multimedia, General System Characteristics for Multimedia, Level of Impact for Multimedia, Multimedia Point Analysis, Unadjusted Multimedia Points

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