S. Sridhar

Work place: Faculty of ECE, Lendi Institute of Engineering & Technology Andhra Pradesh, INDIA

E-mail: sridhar.vskp@gmail.com


Research Interests: Image Processing, Image Manipulation, Image Compression, Neural Networks, Artificial Intelligence


Siripurapu Sridhar, male, is a Faculty member of Electronics and Communication Engineering Department at LENDI Institute of Engineering and Technology, India. He received his bachelor’s degree in Electronics and Communication Engineering in the year 2000 and MTech in the year 2005..He has Fourteen years of experience in teaching under-graduate and post-graduate students. His research areas of interest include VLSI Design, Digital Image Processing, Digital Image Compression and Artificial Neural networks.

Author Articles
Wavelet Transform Techniques for Image Compression – An Evaluation

By S. Sridhar P. Rajesh Kumar K.V.Ramanaiah

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.

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