On scalable coding of hidden Markov sources
While several real world signals, such as speech, image and sensor network data, are modeled as hidden Markov sources (HMS) for recognition and analysis applications, their typical compression exploits temporal correlations by modeling them as simple (non-hidden) Markov sources. However, the inherent hidden Markov nature of these sources implies that an observed sample depends, in fact, on all past observations, thus rendering simple Markov modeling suboptimal. Motivated by this realization, previous work from our lab derived a technique to optimally quantize and compress HMS. In this paper we build on, and considerably extend, these results to the problem of scalable coding of HMS. At the base layer, as proposed earlier, the approach tracks an estimate of the state probability distribution and adapts the encoder structure accordingly. At the enhancement layer, the state probability distribution is refined using available information from past enhancement layer reconstructed samples, and this refined estimate is further combined with quantization information from the base layer, to effectively characterize the probability density of the current sample conditioned on all available information. We update code parameters on the fly, at each observation, at both the encoder and the decoder and at both layers. Experimental results validate the superiority of the proposed approach with considerable gains over standard predictive coding employing a simple Markov model.