Improving the performance of optimal joint decoding

The optimal joint decoder utilizing NLIVQ (Nonlinear Interpolative Vector Quantization) introduced by Gersho in [1] results in vector quantizers which have reduced encoding complexity at the expense of coding performance loss due to the inferiority of their space-filling property. We show a method of improving a high resolution NLIVQ codebook by partitioning its cells in such a way that the resulting lower resolution codebook consists of cells with better space-filling properties. The resolution reduction method is also extended to the case where the quantizer indices are entropy-constrained. From the simulations it is seen that the unconstrained and constrained entropy versions of the proposed vector quantizer have comparable performance to vector quantizers designed by LBG and ECVQ algorithms.