Abstract

Why does fractal image compression work? What is the implicit image model underlying fractal block coding? How can we characterize the types of images for which fractal block coders will work well? These are the central issues we address. We firstly look in to the evaluation of Fractal central issues for image coding and then merge fractal transform in wavelet domain.We introduce a new wavelet-based framework for analyzing block-based fractal compression schemes using wavelet fractal tree (WFT). Within this framework we are able to draw upon insights from the well-established transform coder paradigm in order to address the issue of why fractal block coders work. We show that fractal block coders of the form introduced by Jacquin [1] are a Haar wavelet subtree quantization scheme. We examine a generalization of this scheme to smooth wavelets with additional vanishing moments. The performance of our generalized coder is comparable to the best results in the literature for a Jacquin-style coding scheme. Our wavelet framework gives new insight into the convergence properties of fractal block coders, and leads us to develop an unconditionally convergent scheme with a fast decoding algorithm. Our experiments with this new algorithm indicate that fractal coders derive much of their effectiveness from their ability to efficiently represent wavelet-Fractal tree compression (WFT). Finally, our framework reveals some of the fundamental limitations of pure current fractal compression schemes. We introduce a new Wavelet Huffman based Set Partitioning in Hierarchical Trees (SPIHT) scheme for image compression and got the better fidelity than pure fractal and existing SPIHT methods. Owing to the fact that microarray images have much selfsimilarity, fractal compressor is evaluated in this paper. Also, for having large compression ratio as well as appropriate image quality, hybrid wavelet-fractal compression method is utilized. Different kinds of pure fractal and wavelet-fractal compression are implemented and compared. The obtained results show that the application of this method for compression of microarray images is better than the state-of-the-art techniques.

Keywords: Microarray image, compression, selfsimilarity, pure fractal compression, wavelet-fractal compression.

(P1150803005, 828 KB)

Biographies:

Prof. Yadav D. M. born on 24th April 1972. He perceived bachelor of Engineering BE (EC) from Dr.B.A.M.U, Aurangabad, maharashtra, India in year 1993. After completion of bachelor degree he joined profession as a lecturer in Amrut Vahini College of Engineering, Sangamner, completed master of Engineering ME (EC) from Shivaji University, Kolhapur in Jan-2004. He has published nine papers in national, six in international and one IEEE digital library publication. He is pursuing Ph.D. in the area Image compression in Hybrid nature form BVD University Pune. Currently he is working as Assistant Professor in Rajarshi Shahu College of Engorging Pune, India.

Dr. Bormane D. S. born on 2nd March 1964. He perceived bachelor of engineering degree in BE (EC) from Dr.B.A.M.U, Aurangabad, Maharashtra, India in 1987, Masters Degree in electronics from Shivaji University, Kolhapur in Jan 1996 and Doctoral degree in Electronics and Computer science engineering from S.R.T.M University, Nanded, India. He is having 20 years of teaching experience. He has Presented/published 45 research papers in image processing area in different National/International conferences and 5 papers in Journals and also published one book on VLSI Design. Presently five researchers working with him. He is currently working as a Principal in Rajarshi Shahu College of Engineering, Pune India.

BibTex:

@ARTICLE{P1150803005,

AUTHOR = {Yadav D.M and D. S. Bormane},

TITLE = {Evaluation of Pure Fractal and Wavelet-Fractal Image Compression Techniques},

JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing, GVIP},

YEAR = {2008},

VOLUME = {08},

ISSUE ={II},

PAGES={37--44}

}

(P1150803005, 828 KB)