Abstract:
	The talk will present insights and new results on the quantization of frame expansions.
	The theory of frames in Hilbert spaces allows for a unified view of the sampling-and-reconstruction process.
	This view encompasses  diverse sampling paradigms, such as the Wavelt transform, non-uniform sampling,
	fiilterbanks, and sampling of non-banlimited signals. However, quantization
	techniques for general frame expansions have begun to be studied only in recent years.
	Simple scalar quantization of frame coefficients is currently well understood.
	However, the generalization of more sophisticated sequential quantization schemes,
	such as Sigma-Delta modulation, for the task of quantizing frame expansions, still
	poses interesting problems. Some of the latter can be stated as the following questions,
	to be answered during the talk: "Is it possible to achieve optimal quantization based only upon sampled data?".
	"Can optimal quantization be achieved by means of a sequential algorithm?". 
	"Under what conditions can a sequential quantization algorithm without "look ahead" be optimal?"
	"How can Model Predictive Control techniques be applied to the quantization of general frame expansions?"