Work place: Department of ECE, YSR Engineering College of Yogi Vemana University, Andhra Pradesh, INDIA
Research Interests: Data Structures and Algorithms, Image Processing, Computer Networks, Image Manipulation, Image Compression, Neural Networks
Dr.K.Venkata Ramanaiah is currently working as Associate Professor & HOD of Electronics and Communication Engineering Department at YSR Engineering College of Yogi Vemana University. He received his Mtech and PhD from JNTU Hyderabad. He published papers in Many Reputed international Journals and various national, international conferences. He Has teaching experience of around 21 years. His research areas of interest are Digital Image Processing, VLSI Architectures and Neural Networks etc.
DOI: https://doi.org/10.5815/ijigsp.2014.07.06, Pub. Date: 8 Jun. 2014
We introduce a new generation functionally distinct redundant free Modified Dual Tree Complex Wavelet structure with improved orthogonality and symmetry properties. Traditional Dual Tree Complex Wavelets Transform (DTCWT), which incorporates two operationally similar, procedurally different Discrete Wavelet Transform (DWT) trees, is inherently redundant and computationally complex. In this paper, we propose Symmetrically Modified DTCWT (SMDTCWT) to explore the close relationships between the wavelet coefficients from the real and imaginary tree of the dual-tree CWT with an advent of a Quadrature Filter. This exploitation can reduce the level of redundancy that currently exists in a dual-tree wavelet system and decrease the computational complexity .Some of the primary constraints include that the designed algorithm should be satisfying the Hilbert transform pair condition and should have high coding gain, good directional sensitivity, and sufficient degree of regularity.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2014.02.07, Pub. Date: 8 Jan. 2014
A vital problem in evaluating the picture quality of an image compression system is the difficulty in describing the amount of degradation in reconstructed image, Wavelet transforms are set of mathematical functions that have established their viability in image compression applications owing to the computational simplicity that comes in the form of filter bank implementation. The choice of wavelet family depends on the application and the content of image. Proposed work is carried out by the application of different hand designed wavelet families like Haar, Daubechies, Biorthogonal, Coiflets and Symlets etc on a variety of bench mark images. Selected benchmark images of choice are decomposed twice using appropriate family of wavelets to produce the approximation and detail coefficients. The highly accurate approximation coefficients so produced are further quantized and later Huffman encoded to eliminate the psychovisual and coding redundancies. However the less accurate detailed coefficients are neglected. In this paper the relative merits of different Wavelet transform techniques are evaluated using objective fidelity measures- PSNR and MSE, results obtained provide a basis for application developers to choose the right family of wavelet for image compression matching their application.[...] Read more.
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