Improved hidden Markov models in the wavelet-domain

Abstract

Wavelet-domain hidden Markov models (HMMs), in particular the hidden Markov tree (HMT) model, have been introduced and applied to signal and image processing, e.g., signal denoising. We develop a simple initialization scheme for the efficient HMT model training and then propose a new four-state HMT model called HMT-2. We find that the new initialization scheme fits the HMT-2 model well. Experimental results show that the performance of signal denoising using the HMT-2 model is often improved over the two-state HMT model developed by Crouse et al. (see ibid., vol.46, p.886-902, 1998).