Conditional entropy-constrained vector quantization of linear predictive coefficients

Vector quantization (VQ) followed by entropy coding is used to compress linear predictive coefficients (LPCs) of speech into a variable-rate representation with distortion. It is shown in the LPC case that when a conditional entropy coder is used i.e., when the entropy code used for the current codeword is conditioned on the previous codeword, then a conditional version of entropy constrained vector quantizer (ECVQ) outperforms a conditional version of the straightforward approach by over 27%. Thus, conditioning restores the usual gain of ECVQ over standard VQ. This 27% reduction in bit rate is over and above the 42% reduction in bit rate already obtained by using a conditional rather than memoryless entropy coder in the straightforward approach. The product of these reductions is an overall reduction of 60% in average rate from the original, fixed-rate LPC system