Advances in residual vector quantization (RVQ) are surveyed. Definitio
ns of joint encoder optimality and joint decoder optimality are discus
sed, Design techniques for RVQ's with large numbers of stages and gene
rally different encoder and decoder codebooks are elaborated and exten
ded. Fixed-rate RVQ's, and variable-rate RVQ's that employ entropy cod
ing are examined. Predictive and finite state RVQ's designed and integ
rated into neural-network based source coding structures are revisited
, Successive approximation RVQ's that achieve embedded and refinable c
oding are reviewed, A new type of successive approximation RVQ that va
ries the instantaneous block rate by using different numbers of stages
on different blocks is introduced and applied to image waveforms, and
a scalar version of the new residual quantizer is applied to image su
bbands in an embedded wavelet transform coding system.