Channel-optimised trellis source coding for the memoryless Gaussian channel and applications to spectral parameter coding

The trellis source coder is a high performance source coder that operates at relatively low complexity. Channel-optimised (CO) trellis source coding consists of a jointly designed source encoder and decoder for a given noisy channel. As such, the characteristics of the channel are an integral part of the overall design. This thesis examines various methods of operation on the additive white Gaussian noise ( A W G N ) channel and provides an application to speech spectral parameter coding. To achieve good performance on the A W G N channel consideration must be given to using the continuous or real information provided by the channel. This work describes a number of systems that, variously, use different degrees of quantized channel information. If the decoder is constrained to accept information at the same rate that the encoder provides, it is apparent that the use of a-priori information can improve performance. While maximum a-posteriori (MAP) detection can considerably improve performance, such a system is not jointly designed. Hence a simple decision-feedback detector is proposed and a joint system is developed. Performance for the Gauss-Markov source is compared favourably against maximum likelihood (hard decision) and M A P detection. The second system partitions the channel output space into four regions (symmetric about the origin) converting the channel into a binary input, 4-ary output discrete memoryless channel (DMC). The decoder operates directly with the soft-decision information. The third system is estimation based. The decoder is an optimum, non-linear estimator that accepts continuous information from the channel. This system marginally outperforms the previous system indicating that 4-level quantization realises most of the gain of the infinite-level estimator. Further improvements to this system are obtained by extending the scalar trellis to a two-dimensional vector trellis which also enables signalling in two dimensions. The extension to a vector alphabet (QPSK signalling) yields a quite reasonable improvement without increased encoder computational complexity. A system representing a joint design of trellis encoder, channel, estimator decoder and modulator is consid-

[1]  M. Reza Soleymani A new tandem source-channel trellis coding scheme , 1994, IEEE Trans. Speech Audio Process..

[2]  A. Stroud Approximate calculation of multiple integrals , 1973 .

[3]  Andrew J. Viterbi,et al.  Principles of Digital Communication and Coding , 1979 .

[4]  Robert M. Gray,et al.  The Design of Trellis Waveform Coders , 1982, IEEE Trans. Commun..

[5]  Charles L. Weber,et al.  Elements of Detection and Signal Design , 1968 .

[6]  Takehiro Moriya Two-Channel Conjugate Vector Quantizer for Noisy Channel Speech Coding , 1992, IEEE J. Sel. Areas Commun..

[7]  Michael W. Marcellin,et al.  Predictive trellis coded quantization of speech , 1990, IEEE Trans. Acoust. Speech Signal Process..

[8]  Michael W. Marcellin,et al.  Trellis coded quantization of memoryless and Gauss-Markov sources , 1990, IEEE Trans. Commun..

[9]  D. Rowe,et al.  A robust 2400 bit/s MBE-LPC speech coder incorporating joint source and channel coding , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  N. Farvardin,et al.  Quantizer design in LSP speech analysis and synthesis , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[11]  A. Gray,et al.  Implementation and comparison of two transformed reflection coefficient scalar quantization methods , 1980 .

[12]  R. Steele,et al.  Logarithmic PCM weighted QAM transmission over Gaussian and Rayleigh fading channels , 1987 .

[13]  W. Pearlman Sliding-Block and Random Source Coding with Constrained Size Reproduction Alphabets , 1982, IEEE Trans. Commun..

[14]  Joel Max,et al.  Quantizing for minimum distortion , 1960, IRE Trans. Inf. Theory.

[15]  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.

[16]  Nam C. Phamdo,et al.  A unified approach to tree-structured and multistage vector quantization for noisy channels , 1993, IEEE Trans. Inf. Theory.

[17]  S. Wilson,et al.  Trellis encoding of continuous-amplitude memoryless sources (Corresp.) , 1977, IEEE Trans. Inf. Theory.

[18]  Mikael Skoglund,et al.  Vector quantization over a noisy channel using soft decision decoding , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  C.-E. Sundberg,et al.  Optimum Weighted PCM for Speech Signals , 1978, IEEE Trans. Commun..

[20]  Salvatore D. Morgera,et al.  Image coding for noisy channels , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[21]  Biing-Hwang Juang,et al.  Optimal quantization of LSP parameters , 1993, IEEE Trans. Speech Audio Process..

[22]  L. Turner Key Papers in the Development of Information Theory , 1975 .

[23]  Gottfried Ungerboeck,et al.  Channel coding with multilevel/phase signals , 1982, IEEE Trans. Inf. Theory.

[24]  UngerboeckG. Trellis-coded modulation with redundant signal sets Part II , 1987 .

[25]  M. Reza Soleymani,et al.  Codebook design for trellis quantization using simulated annealing , 1993, IEEE Trans. Speech Audio Process..

[26]  Takehiro Moriya,et al.  Training method of the excitation codebook for CELP , 1993, EUROSPEECH.

[27]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[28]  Andrew J. Viterbi,et al.  Trellis Encoding of memoryless discrete-time sources with a fidelity criterion , 1974, IEEE Trans. Inf. Theory.

[29]  Takehiro Moriya,et al.  Combined Source-Channel Coding of LSP Parameters Using Multi-Stage Vector Quantization , 1993 .

[30]  Andrew Perkis,et al.  Joint source and channel trellis coding of line spectrum pair parameters , 1992, Speech Commun..

[31]  K. Paliwal,et al.  Efficient vector quantization of LPC parameters at 24 bits/frame , 1990 .

[32]  J. Makhoul,et al.  Quantization properties of transmission parameters in linear predictive systems , 1975 .

[33]  Andrew Perkis,et al.  Joint source and channel coding of line spectrum pairs , 1991, EUROSPEECH.

[34]  Biing-Hwang Juang,et al.  Line spectrum pair (LSP) and speech data compression , 1984, ICASSP.

[35]  Nariman Farvardin,et al.  Optimal block cosine transform image coding for noisy channels , 1990, IEEE Trans. Commun..

[36]  Nariman Farvardin,et al.  Joint design of block source codes and modulation signal sets , 1992, IEEE Trans. Inf. Theory.

[37]  Philipp W. Besslich,et al.  Shape-Gain Vector Quantization for Noisy Channels with Applications to Image Coding , 1992, IEEE J. Sel. Areas Commun..

[38]  M. Reza Soleymani,et al.  Trellis quantization with MAP detection for noisy channels , 1992, IEEE Trans. Commun..

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

[40]  Aaron D. Wyner,et al.  Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959. , 1993 .

[41]  Andrew Perkis,et al.  Efficient vector quantisation of LPC parameters for noisy channels , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[42]  R. A. Kennedy,et al.  Maximum a posteriori decision feedback detection , 1992 .

[44]  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.