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Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (e.g., using the expectation-maximization algorithm). In this paper, we greatly simplify the HMTdoi:10.1109/83.931100 pmid:18249679 fatcat:hp6zohgmf5dulnsy4zf4xy5zpu