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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 11, 2016.
Abstract: Statistical signal modeling using hidden Markov model is one of the techniques used for image compression. Wavelet based statistical signal models are impractical for most of the real time processing because they usually represent the wavelet coefficients as jointly Gaussian or independent to each other. In this paper, we build up an algorithm that succinctly characterizes the interdependencies of wavelet coefficients and their Non-Gaussian behavior especially for image compression. This is done by extracting the combine feature of hidden Markov model and Wavelet transformation that gives us comparatively better results. To estimate the parameter of wavelet based Hidden Markov model, an efficient expectation maximization algorithm is developed.
Muhammad Usman Riaz, Imran Touqir and Maham Haider, “Wavelet-based Image Modelling for Compression Using Hidden Markov Model” International Journal of Advanced Computer Science and Applications(IJACSA), 7(11), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071139
@article{Riaz2016,
title = {Wavelet-based Image Modelling for Compression Using Hidden Markov Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071139},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071139},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {11},
author = {Muhammad Usman Riaz and Imran Touqir and Maham Haider}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.