Fast VQ codebook generation via Pattern Reduction

In this paper, we present a simple but fast codebook generation algorithm, called PREGLA (pattern reduction enhanced GLA). The proposed algorithm is fundamentally different from the previous approaches in that the latter focuses on reducing the size of the codebook whereas the former focuses on using pattern reduction to reduce the computation time. The proposed algorithm is motivated by the observation that input vectors that are ldquostaticrdquo during the training process can be considered as part of the final solutions and thus can be compressed and removed to eliminate the redundant computations at the later iterations of the training process. To evaluate the performance of the proposed algorithm, we compare the proposed algorithm with ldquoefficientrdquo VQ algorithms published recently such as nearest partition set search, fast vector quantization algorithm, and standard GLA. Our experimental results indicate that the proposed algorithm can reduce the computation time from 27.86% up to about 80.45% compared to that of standard GLA and other fast GLA-based algorithms alone.