Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation

Joint source-channel decoding based on residual source redundancy is an effective paradigm for error-resilient data compression. While previous work only considered fixed rate systems, the extension of these techniques for variable-length encoded data was previously independently proposed by the authors, Park and Miller (see Proc. of Conf. on Info. Sciences and Systems, Princeton, N.J., 1998) and by Demir and Sayood (see Proc. of the Data Compression Conf., Snowbird, U.T., p.139-48, 1998). In this paper, we describe and compare the performance of a computationally complex exact maximum a posteriori (MAP) decoder, its efficient approximation, an alternative approximate MAP decoder, and an improved version of this decoder suggested here. Moreover, we evaluate several source and channel coding configurations. Our results show that the approximate MAP technique from Park et al. outperforms other approximate methods and provides substantial error protection to variable-length encoded data.

[1]  N. Phamdo,et al.  Optimal Detection of Discrete Markov Sources Over Discrete Memoryless Channels - Applications to Combined Source-Channel Coding , 1993, Proceedings. IEEE International Symposium on Information Theory.

[2]  Khalid Sayood,et al.  Use of residual redundancy in the design of joint source/channel coders , 1991, IEEE Trans. Commun..

[3]  K. P. Subbalakshmi,et al.  Optimal decoding of entropy coded memoryless sources over binary symmetric channels , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[4]  Khalid Sayood,et al.  Joint source/channel coding for variable length codes , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[5]  Thomas E. Fuja,et al.  Exploiting the residual redundancy in motion estimation vectors to improve the quality of compressed video transmitted over noisy channels , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[6]  David J. Miller,et al.  A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden Markov models , 1998, IEEE Trans. Commun..

[7]  Thomas E. Fuja,et al.  Joint source-channel decoding of variable-length encoded sources , 1998, 1998 Information Theory Workshop (Cat. No.98EX131).

[8]  Robert M. Gray,et al.  The design of joint source and channel trellis waveform coders , 1987, IEEE Trans. Inf. Theory.

[9]  Fady Alajaji,et al.  Quantization of memoryless and Gauss-Markov sources over binary Markov channels , 1997, IEEE Trans. Commun..

[10]  P. Wintz,et al.  Quantizing for Noisy Channels , 1969 .

[11]  David J. Miller,et al.  Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation , 2000, IEEE Trans. Commun..

[12]  John G. Proakis,et al.  Digital Communications , 1983 .

[13]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[14]  David J. Miller,et al.  Low-delay optimal MAP state estimation in HMM's with application to symbol decoding , 1997, IEEE Signal Processing Letters.

[15]  Khalid Sayood,et al.  Joint source/channel coding for variable length codes , 2000, IEEE Trans. Commun..

[16]  Nam C. Phamdo,et al.  Optimal detection of discrete Markov sources over discrete memoryless channels - applications to combined source-channel coding , 1994, IEEE Trans. Inf. Theory.