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 ndimensional 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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Leonardo H. Fonteles, Marc Antonini
• Keywords: ICIP 2007, Image Processing, Lattice Vector Quantizer, Optimal Bit Allocation, Propose Efficient Solutions, Uniform Scalar Quantization, Vector Quantization