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We combine backward adaptive quantization with the scalar-vector quantizer (SVQ) and the trellis coded quantizer (TCQ) both of which have an underlying scalar quantizer (USQ) in their structure. The resulting adaptive scalar-vector quantizer (ASVQ) and adaptive trellis coded quantizer (ATCQ) redesign the USQ based on the past quantized outputs. The adaptive quantizers require no side information and also outperform the SVQ and the TCQ, respectively, when the input signal is non-stationary. For an input sequence from a bimodal source switching infrequently between two Gaussian distributions with the same mean and different variances, both adaptive quantizers achieve performance gains of more than 1.3 dB over the non-adaptive quantizers designed on the training set from the same bimodal source. Also the adaptive quantizers demonstrate minimal performance degradation due to adaptation when stationary inputs are considered.