Kodati Satya Prasad

Work place: Dept. of ECE, JNTUK, Kakinada, Andhra Pradesh, India

E-mail: k.satyaprasad.1955@ieee.org


Research Interests: Physics, Physics & Mathematics, Computational Physics


Dr.Kodati Satya Prasad received B.Tech (ECE) degree from JNT University, Hyderabad in 1977, M.E (Communication Systems) from the University of Madras in 1979, Ph.D. from IIT, Madras in 1989. He has more than 35 years of experience in teaching and 20 years of R & D. He is an expert in Signals & Systems, Communications, Digital Signal Processing, Radar, and Telemetry. He produced 10 Ph.D. candidates and guiding 10 research scholars. He authored Electronic Devices and Circuits textbook. He held different positions in his career like Head of the Department, Vice Principal, Principal of JNTU College Engineering, Anantapur, Director of Evaluation, and Rector of JNT University, Kakinada, A.P, India. . He published more than 200 technical papers in national and International journals and conferences. At present, he is the Professor in dept. of ECE, JNTUK, Kakinada. He received Patent for his research work in 2015. He has membership in many professional societies like IEEE, IETE, ISTE, and IE

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

By Kethepalli Mallikarjuna Kodati Satya Prasad Makam Venkata Subramanyam

DOI: https://doi.org/10.5815/ijigsp.2016.01.07, 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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