High Dimension Lattice Vector Quantizer Design for Generalized Gaussian Distributions

LVQ is a simple but powerful tool for vector quantization and can be viewed as a vector generalization of uniform scalar quantization. Like VQ, LVQ is able to take into account spatial dependencies between adjacent pixels as well as to take advantage of the n-dimensional space filling gain. However, the design of a lattice vector quantizer is not trivial particularly when one wants to use vectors with high dimensions. Indeed, using high dimensions involves lattice codebooks with a huge population that makes indexing difficult. On the other hand, in the framework of wavelet transform, a bit allocation across the subbands must be done in an optimal way. The use of VQ and the lack of non asymptotical distortion-rate models for this kind of quantizers make this operation difficult. In this work we focus on the problem of efficient indexing and optimal bit allocation and propose efficient solutions.