Switched-adaptive interframe vector prediction with binary-tree searched predictors

An approach for reducing the complexity of the switched-adaptive interframe vector prediction (SIVP) that is used for coding speech spectrum envelopes is proposed in this paper. To facilitate the search through the set of switched predictors used for prediction of the input LSF (line spectral frequency) vector, the predictors are organized in a binary tree structure. For a conventional full-searched SIVP coder with N=2^b predictors, predictions must be performed by all of them in order to determine the best one, while only 2b predictions are sufficient for the proposed binary tree-searched coder. A design procedure for obtaining optimal binary tree-structured predictors is given. The effectiveness of the proposed coder is evaluated and the results are compared to the baseline full-searched coders as a function of the number of predictors and the resolution of the vector quantizers used for quantization of the prediction residual. A discussion of possible applications of the proposed coder is also given