Quantization of filter bank frame expansions through moving horizon optimization

This paper describes a novel approach to quantization in oversampled filter banks. The new technique is based on moving horizon optimization, does not rely on an additive white noise quantization model and allows stability to be explicitly enforced in the associated nonlinear feedback loop. Moreover, the quantization structure proposed here includes ΣΔ and linear predictive subband quantizers as a special case and, in general, outperforms them.