  This paper proposes a parallel approach for the Vector Quantization (VQ)
problem in image processing. VQ deals with codebook generation from the input
training data set and replacement of any arbitrary data with the nearest
codevector. Most of the efforts in VQ have been directed towards designing
parallel search algorithms for the codebook, and little has hitherto been done
in evolving a parallelized procedure to obtain an optimum codebook. This
parallel algorithm addresses the problem of designing an optimum codebook using
the traditional LBG type of vector quantization algorithm for shared memory
systems and for the efficient usage of parallel processors. Using the codebook
formed from a training set, any arbitrary input data is replaced with the
nearest codevector from the codebook. The effectiveness of the proposed
algorithm is indicated.
