REDUCED STORAGE VQ VIA SECONDARY QUANTIZATION

This paper introduces methods for reducing the table storage required for encoding and decoding with unstructured vector quantization (UVQ) or tree-structured vector quantization (TSVQ), Specifically, a low-sto rage secondary quantizer is used to compress the codevectors (and test vectors) of the primary quantizer, The relative advantages of uniform and nonuniform secondary quantization are investigated, An LEG-like al gorithm that optimizes the primary UVQ codebook for a given secondary codebook and another that jointly optimizes both primary and secondary codebooks are presented, In comparison to conventional methods, it is found that significant storage reduction is possible (typically a fac tor of two to three) with little loss of signal-to-noise ratio (SNR). Moreover, when reducing dimension is considered as another method of r educing storage, it is found that the best strategy is a combination o f both, The method of secondary quantization is also applied to TSVQ t o reduce the table storage required for both encoding and decoding, It is shown that by exploiting the correlation among the testvectors in the tree, both encoder and decoder storage Can be significantly reduce d with little loss of SNR-by a factor of about four (or two) relative to the conventional method of storing testvectors (or test hyperplanes ).