Multi-step optimal quantization in oversampled filter banks

Abstract

Using concepts from the receding horizon control framework, we propose an approach to quantization in oversampled filter banks. The key idea is to pose the quantization problem as a multi-step optimization problem, where the decision variables are restricted to belong to a finite set. It is shown that the resulting architecture yields enhanced performance when compared to the well-known noise shaping coder. In particular, the quantizer proposed can be tuned with stability concepts in mind.