Lattice/Trellis based Fixed-rate Entropy-constrained Quantization

The fixed-rate entropy-constrained vector quantizer draws its motivation from the large gap in the performance of the optimal entropy-constrained scalar quantizer (ESCQ) and the fixed-rate LMQ for most non-uniform sources and tries to bridge this gap while maintaining a fixed-rate output. Having a fixed-rate output avoids all problems associated with a variable rate output such as error propagation and buffering. For the task of codebook search in a fixed-rate entropy-constrained quantizer, one can use the dynamic programming approach to achieve an optimum performance. However, for high dimension and rates, the implementation complexity of dynamic programming approach is not affordable by the most practical systems. In this thesis, we introduce two new low complexity algorithms for the codebook search in a fixed-rate entropy-constrained vector quantizer. It is shown that both schemes are offering the same performance as that of dynamic programming approach while they are reducing the complexity substantially (for most important class of sources). We also propose a decoder for the fixed-rate entropy-constrained vector quantizer to improve its performance in transmission over a noisy channel. In order to add quantization packing gain, we apply the idea of tail-biting to the trellis coded quantization combined with fixed-rate entropy-constrained quantization. The tail-biting trellis significantly reduces the required block length and also mitigates the effect of error propagation.