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 non-Gaussian, long-range-dependent network traffic.
| Original language | English (US) |
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| Title of host publication | Conference Record of the Asilomar Conference on Signals, Systems and Computers |
| Editors | M.P. Farques, R.D. Hippenstiel |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 95-100 |
| Number of pages | 6 |
| Volume | 1 |
| State | Published - 1998 |
| Event | Proceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA Duration: Nov 2 1997 → Nov 5 1997 |
Other
| Other | Proceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) |
|---|---|
| City | Pacific Grove, CA, USA |
| Period | 11/2/97 → 11/5/97 |
ASJC Scopus subject areas
- Hardware and Architecture
- Signal Processing
- Electrical and Electronic Engineering