DocumentCode :
3420709
Title :
Joint source-channel decoding of multiple description quantized Markov sequences
Author :
Wu, Xiaolin ; Wang, Xiaohan ; Wang, Jia
Author_Institution :
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont., Canada
fYear :
2006
fDate :
28-30 March 2006
Firstpage :
103
Lastpage :
112
Abstract :
This paper proposes a framework for joint source-channel decoding of Markov sequences that are coded by a fixed-rate multiple description quantizer (MDQ), and transmitted via a lossy network. This framework is suited for lossy networks of primitive energy-deprived source encoders. Our technical approach is one of maximum a posteriori probability (MAP) sequence estimation that exploits both the source memory and the correlation between different MDQ descriptions. We solve the MAP estimation problem by computing the longest path in a weighted directed acyclic graph, at a complexity of O(L2NK), where N is the number of source symbols in the input sequence, K is the number of MDQ descriptions, and L is the number of codewords of the central quantizer. If the source sequence is Gaussian Markovian, the decoder complexity can be reduced to O(LNK). For MDQ-compressed Markov sequences impaired by both bit errors and erasure errors, the performance of joint source-channel MAP decoder can be 6 dB higher than the conventional hard-decision decoder. Furthermore, the new MDQ decoding technique unifies the treatments of different subsets of the K descriptions available at the decoder, circumventing the thorny issue of requiring up to 2K - 1 MDQ side decoders.
Keywords :
Markov processes; combined source-channel coding; directed graphs; maximum likelihood decoding; maximum likelihood sequence estimation; sequences; Gaussian Markovian; MAP sequence estimation; Markov sequences; codewords; decoder complexity; fixed-rate multiple description quantizer; joint source-channel decoding; lossy network; lossy networks; maximum a posteriori probability sequence estimation; primitive energy-deprived source encoders; weighted directed acyclic graph; Centralized control; Codecs; Computer networks; Decoding; Entropy coding; Intelligent sensors; Lattices; Redundancy; Sensor phenomena and characterization; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Compression Conference, 2006. DCC 2006. Proceedings
ISSN :
1068-0314
Print_ISBN :
0-7695-2545-8
Type :
conf
DOI :
10.1109/DCC.2006.41
Filename :
1607245
Link To Document :
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