CONSTRAINED-STORAGE QUANTIZATION OF MULTIPLE VECTOR SOURCES BY CODEBOOK SHARING

Many coding problems require the design of a family of codebooks for separate vector quantization (VQ) of each of a set of vector sources which collectively characterize a segment of a signal. An example is the quantization of a large number of transform coefficients by separately quantizing subsets of coefficients that are treated as vectors. While encoding complexity of VQ can be circumvented by such techniques as tree-structured codebooks, the exponential growth of storage complexity with resolution could discourage the use of VQ in emerging signal compression applications where high fidelity reproduction is required. A codebook sharing technique, called constrained storage vector quantization (CSVQ) is introduced which offers a convenient and optimal way of trading off performance against storage. The technique can be used in conjunction with tree-structured VQ and other structured VQ techniques that alleviate the search complexity obstacle. The effectiveness of CSVQ is illustrated for coding transform coefficients of audio signals with multistage VQ.