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On scalable coding of hidden Markov sources
2016
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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 (nonhidden) 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,
doi:10.1109/icassp.2016.7472433
dblp:conf/icassp/SalehifarNR16
fatcat:rzwzfxvjcfgi7dg4ialrqclfdi