On the performance of tree-structured vector quantization

Tree-structured vector quantization (TSVQ) is a commonly used structured vector quantization (VQ) technique that permits much faster quantization and incurs a little more distortion than full search of an optimal unstructured VQ. By exploiting two recently developed asymptotic quantization formulas it is shown that the mean-squared error of TSVQ and, moreover, the probability density of the magnitude of its errors support the hypothesis that TSVQ has an optimal point density and that its cells consist of various rectangular-like polyhedra