Adaptive vector quantization for waveform coding

The authors discuss an adaptive algorithm for vector quantization (VQ) whose codebook is constantly being updated by the most recent past input vector. Using this approach, the VQ codebook is continuously training on new data, therefore eliminating the need for long training-sequence processes as with regular VQ . If identical adaptation rules are used at the encoder and decoder, no side information is sent and the system behaves as backward-looking adaptive scalar quantizers. Adaptive-vector-quantization (AVQ) also has the advantage of less degradation with varying statistics on the input signal as compared with regular VQ. Simulation results are presented outlining the bit-rate-vs.-SNR (signal-to-noise ratio) performance for the AVQ system. It is shown that, although AVQ performs 1-3-dB SNR worse than regular VQ (at 1 b/sample) inside the training sequence, AVQ outperforms regular VQ by as much as 12 dB outside the training sequence. This improvement implies the realizability of adaptive VQ for real-time digital coding systems