Parallelization of the LBG Vector Quantization Algorithm for Shared Memory Systems

TL;DR

This paper introduces a parallelized version of the LBG vector quantization algorithm optimized for shared memory systems, enabling efficient codebook generation and data replacement in image processing tasks.

Contribution

It presents a novel parallel algorithm for the LBG vector quantization process tailored for shared memory architectures, improving efficiency in codebook optimization.

Findings

Effective parallelization of the LBG algorithm demonstrated

Improved efficiency in codebook generation shown

Parallel approach suitable for shared memory systems

Abstract

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.