engleski

Sparse vector linear predictor matrices with multidiagonal structure

A modification of the classical vector linear prediction (VLP) problem is presented. The introduced technique called the sparse VLP (sVLP) is based on the assumption that each component of a single LSF vector is highly correlated only to a few neighboring vector components of consecutive vectors, while the correlation between distant vector components can be ignored. This leads to simplification of predictor matrices in a way that for a chosen number of neighboring components, predictor matrices obtain multidiagonal form. It is shown that prediction gain resulting from sVLP is only slightly lower than for the case of full matrix predictors but with significant reduction of computation, both for coding and predictor design.

vector linear prediction; VLP; LSF; multidiagonal matrices

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