  The paper studies optimal coding of hidden Markov sources (HMS), which
represent a broad class of practical sources obtained through noisy acquisition
processes, beside their explicit modeling use in speech processing and
recognition, image understanding and sensor networks. A new fundamental source
coding approach for HMS is proposed, based on tracking an estimate of the state
probability distribution, and is shown to be optimal. Practical encoder and
decoder schemes that leverage the main concepts are introduced. An iterative
approach is developed for optimizing the system. It also focuses on a
significant extension of the optimal HMS quantization paradigm. It proposes a
new approach for scalable coding of HMS which accounts for all the available
information while coding a given layer. Simulation results confirm that these
approaches significantly reduce the reconstructed distortion and substantially
outperform existing techniques.
