Robust vector quantization for noisy channels using soft decision and sequential decoding

This paper considers the optimization of a vector quantizer whose indices are transmitted over a channel with known noise statistics (pdf). A decoder is introduced which uses a linear combination of VQ codebook entries with weights depending on the received signal. Rather than using a received index to select an entry in the codebook (which implies a hard decision on the index), the decoder uses the received signal and the channel statistics to weight the codebook entries in order to produce a good reconstructed signal. Borrowing a term used in digital communications, we will say that the optimal decoder uses a “soft” decision rule. The corresponding approach will be called Soft Decision Vector Quantization (SDVQ). It is shown that SDVQ achieves a significant performance improvement over both the source optimized VQ and the channel optimized VQ (the latter is based on known channel transition probabilities). For correlated sources, it is shown that SDVQ performance can be further improved by using sequential decoding. Results are presented for an AR(1) source and for speech line spectral pair (LSP) parameter quantization at 24 bits/frame.