Abstract: A quantization apparatus comprises: a first quantization module for performing quantization without an inter-frame prediction; and a second quantization module for performing quantization with an inter-frame prediction, and the first quantization module comprises: a first quantization part for quantizing an input signal; and a third quantization part for quantizing a first quantization error signal, and the second quantization module comprises: a second quantization part for quantizing a prediction error; and a fourth quantization part for quantizing a second quantization error signal, and the first quantization part and the second quantization part comprise a trellis structured vector quantizer.

Abstract: A quantization device includes: a trellis-structured vector quantizer which quantizes a first error vector between an N-dimensional (here, “N” is two or more) subvector and a first predictive vector; and an inter-frame predictor which generates a first predictive vector from the quantized N-dimensional subvector, wherein the inter-frame predictor uses a predictive coefficient comprising an N×N matrix and performs an inter-frame prediction using the quantized N-dimensional subvector of a previous stage.

Abstract: Methods and systems are provided for separating signal-correlated and signal-uncorrelated error components in quantization noise. Such separation leads to a generalization of the conventional rate-distortion optimization problem. For the commonly used assumption of a Gaussian process, a quantizer according to this principle is implemented in a straightforward manner using a dithered quantizer and appropriate pre-filters and post-filters. If the penalization of the signal-uncorrelated error component is increased over that of the signal-correlated error component, then the pre-filter emphasizes the signal spectrum more, reducing the differential entropy rate of the pre-filtered signal. Accordingly, the signal-uncorrelated noise is reduced for a given rate.