Computation of Posterior Marginals on Aggregated State Models for Soft Source Decoding

Simon Malinowski 1 Hervé Jégou 2, 3 Christine Guillemot 1
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Optimum soft decoding of sources compressed with variable length codes and quasi-arithmetic codes, transmitted over noisy channels, can be performed on a bit/symbol trellis. However, the number of states of the trellis is a quadratic function of the sequence length leading to a decoding complexity which is not tractable for practical applications. The decoding complexity can be significantly reduced by using an aggregated state model, while still achieving close to optimum performance in terms of bit error rate and frame error rate. However, symbol a posteriori probabilities can not be directly derived on these models and the symbol error rate (SER) may not be minimized. This paper describes a two-step decoding algorithm that achieves close to optimal decoding performance in terms of SER on aggregated state models. A performance and complexity analysis of the proposed algorithm is given.
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Simon Malinowski, Hervé Jégou, Christine Guillemot. Computation of Posterior Marginals on Aggregated State Models for Soft Source Decoding. IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2009, 57 (4), pp.888-892. ⟨10.1109/TCOMM.2009.04.070061⟩. ⟨inria-00394217⟩

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