Title :
Contextual hidden Markov models for wavelet-domain signal processing
Author :
Crouse, Matthew S. ; Baraniuk, Richard G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
Abstract :
Wavelet-domain hidden Markov models (HMMs) provide a powerful new approach for statistical modeling and processing of wavelet coefficients. In addition to characterizing the statistics of individual wavelet coefficients, HMMs capture some of the key interactions between wavelet coefficients. However, as HMMs model an increasing number of wavelet coefficient interactions, HMM-based signal processing becomes increasingly complicated. In this paper, we propose a new approach to HMMs based on the notion of context. By modeling wavelet coefficient inter-dependencies via contexts, we retain the approximation capabilities of HMMs, yet substantially reduce their complexity. To illustrate the power of this approach, we develop new algorithms for signal estimation and for efficient synthesis of nonGaussian, long-range-dependent network traffic.
Keywords :
estimation theory; hidden Markov models; signal processing; statistical analysis; telecommunication traffic; wavelet transforms; HMM-based signal processing; approximation capabilities; complexity; context; contextual hidden Markov models; nonGaussian long-range-dependent network traffic; signal estimation; statistical modeling; synthesis; wavelet coefficient inter-dependencies; wavelet coefficient interactions; wavelet-domain signal processing; Context modeling; Estimation; Hidden Markov models; Network synthesis; Signal processing algorithms; Signal synthesis; Statistics; Telecommunication traffic; Traffic control; Wavelet coefficients;
Conference_Titel :
Signals, Systems & Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-8316-3
DOI :
10.1109/ACSSC.1997.680036