Kethepalli Mallikarjuna

Work place: Dept. of ECE, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India



Research Interests: Computational Science and Engineering, Computational Engineering, Engineering


Kethepalli Mallikarjuna received Bachelor of Engineering degree in Electronics and Communication Engineering from Gulbarga University, Karnataka, India, master degree in Digital Systems and Computer Electronics from Jawaharlal Nehru Technological University, Hyderabad, Andhra Pradesh, India, in 1991 and 2003, respectively. And he is perusing Ph.D. in image processing field in Jawaharlal Nehru Technological University, Kakinada. He has more than 20 years of experience in teaching and 7 years of R & D. He was an Associate Lecture in the department of Electronics and Communication Engineering, Vasavi Polytechnic, Banaganapalli, Andhra Pradesh, during 1993-2000. Since 2003, he is working in Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India. At present he is working as an Associate Professor in the department of ECE of this institute. He has membership in many professional societies. He is the Member of the Institution of Engineers (India)-MIETE, the Fellow of the Institution of Electronics and Telecommunication Engineers-FIETE, the Member of the Indian Society for Technical Education-MISTE, and the Member of the Institution of Electrical and Electronics Engineers-MIEEE.

Author Articles
Image Compression and Reconstruction using Discrete Rajan Transform Based Spectral Sparsing

By Kethepalli Mallikarjuna Kodati Satya Prasad Makam Venkata Subramanyam

DOI:, Pub. Date: 8 Jan. 2016

As a contribution from research conducted by many, various image compression techniques have been developed on the basis of transformation or decomposition algorithms. The compressibility of a signal is seen to be affected by the entropy in the signal. Compressibility is high if the energy distribution is concentrated in fewer coefficients. It is reasonable to expect that sparse signals have a highly compressible nature. Thus, sparse representations have potential uses in image compression techniques. There are many techniques used for this purpose. As an alternative to these traditional approaches, the use of Discrete Rajan Transform for sparsification and image compression was explored in this paper. The simulation results show that higher quality compression can be achieved for images using Discrete Rajan Transform in comparison with other popular transforms like Discrete Cosine Transform, and Discrete Wavelet Transform. The results of the experiment were analyzed on the basis of seven quality measurement parameters – Mean Squared Error, Peak Signal to Noise ratio, Normalized Cross-Correlation, Average Difference, Structural Content, Maximum Difference, and Normalized Absolute Error. It was observed that Discrete Rajan Transform is effective in introducing sparsity in images and thereby improving compressibility.

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