Sparse vector linear prediction with near-optimal matrix structures

Vector linear prediction (VLP) is frequently used in speech and image coding. This paper addresses a technique of reducing the complexity of VLP, named the sparse VLP (sVLP), by decreasing the number of nonzero elements of prediction matrices used for prediction. The pattern of zero and nonzero elements in a matrix, i.e. the matrix structure, is not restricted in the design procedure but is a result of the correlation properties of the input vector process. Mathematical formulations of several criteria for obtaining near-optimal matrix structures are given. The consequent decrease of the sVLP performance compared to the full predictor case can be kept as low as possible by re-optimizing the values of matrix non-zero elements for a resulting sparse structure. Effectiveness of the sVLP is illustrated on vector prediction of the line spectrum frequencies (LSF) vectors and compared to the full predictor VLP.