to a method and apparatus of efficient encoding and decoding of vector quantized data the method and system explores and implements sub division of a quantization vector space comprising class leader vectors and representation of the class leader vectors by a set of class leader root vectors facilitating faster encoding and decoding and reduced storage requirements application_number US 90585210 A description vector quantizers based on structured codebooks generally provide a good compromise between memory requirement computational complexity and quantization performance for practical use for a given input signal probability distribution function pdf the theoretically optimal code vector point density function for a given input signal pdf is well approximated by structured codebooks such as lattice codebooks and permutation codebooks these codebooks are not stored completely in memory and are searched efficiently for codebooks according to the invention knowledge on the probability distribution function pdf is required for the structured codebooks it is not useful to assume a certain statistical characteristic of audio signals worst case assumptions are preferably made so that the designed vq has good performance even for the signal that is hardest to quantize among all signal characteristics gaussian noise has the highest differential entropy therefore following the worst case approach a quantizer for audio coding should preferably be designed to have good performance for gaussian sources spherical vector quantization svq is a quantization that approximates the optimal code vector point density for the gaussian distribution and at the same time can.