Wavelet-based Image Modelling for Compression Using Hidden Markov Model
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 11
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.
Authors and Affiliations
Muhammad Usman Riaz, Imran Touqir, Maham Haider
Keywords
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- EP ID EP397024
- DOI 10.14569/IJACSA.2016.071139
- Views 105
- Downloads 0
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