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Wavelet-based Image Modelling for Compression Using Hidden Markov Model
Wavelet-based Image Modelling for Compression Using Hidden Markov Model
- Source :
- International Journal of Advanced Computer Science and Applications. 7
- Publication Year :
- 2016
- Publisher :
- The Science and Information Organization, 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.
- Subjects :
- Discrete wavelet transform
General Computer Science
Computer science
Gaussian
Stationary wavelet transform
MathematicsofComputing_NUMERICALANALYSIS
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cascade algorithm
Data_CODINGANDINFORMATIONTHEORY
02 engineering and technology
Markov model
Wavelet packet decomposition
symbols.namesake
Wavelet
Expectation–maximization algorithm
0202 electrical engineering, electronic engineering, information engineering
Hidden Markov model
business.industry
Maximum-entropy Markov model
Variable-order Markov model
Second-generation wavelet transform
Wavelet transform
020206 networking & telecommunications
Pattern recognition
symbols
Hidden semi-Markov model
Forward algorithm
Artificial intelligence
business
Image compression
Subjects
Details
- ISSN :
- 21565570 and 2158107X
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- International Journal of Advanced Computer Science and Applications
- Accession number :
- edsair.doi...........76dcd8304b6c3c95c2afd796731d5ba0
- Full Text :
- https://doi.org/10.14569/ijacsa.2016.071139