Classified variable rate residual vector quantization applied to image subband coding

The linear growth with the dimension-rate product of RVQ computation and memory requirements permits practical implementations of RVQs with large dimensions or high rates. This feature is exploited by quantizing low-resolution subbands with small-dimension high-rate RVQs, and high-resolution subbands with large-dimension low-rate RVQs. The RVQ vector sizes vary by a factor of four in parallel with the decimation and up-sampling processes from one resolution level to the next. Two forms of rate allocation are achieved with the RVQ subband system. A type of concentric shell partitioned vector classifier with side information is used to separate noise-like subband vectors from structured subband vectors. For the large-dimension low-rate RVQs, variable rate RVQ with side information permits different numbers of RVQ stages to be used on different vectors within a concentric shell partition class