Optimal nonlinear interpolative vector quantization

A process by which a reduced-dimensionality feature vector can be extracted from a high-dimensionality signal vector and then vector quantized with lower complexity than direct quantization of the signal vector is discussed. In this procedure, a receiver must estimate, or interpolate, the signal vector from the quantized features. The task of recovering a high-dimensional signal vector from a reduced-dimensionality feature vector can be viewed as a generalized form of interpolation or prediction. A way in which optimal nonlinear interpolation can be achieved with negligible complexity, eliminating the need for ad hoc linear or nonlinear interpolation techniques, is presented. The range of applicability of nonlinear interpolative vector quantization is illustrated with examples in which optimal nonlinear estimation from quantized data is needed for efficient signal compression