Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 758734, 9 pages
Research Article

A Hybrid Vector Quantization Combining a Tree Structure and a Voronoi Diagram

1Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan
2Department of Otolaryngology, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan

Received 23 December 2013; Accepted 13 April 2014; Published 4 May 2014

Academic Editor: Xinkai Chen

Copyright © 2014 Yeou-Jiunn Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multimedia data is a popular communication medium, but requires substantial storage space and network bandwidth. Vector quantization (VQ) is suitable for multimedia data applications because of its simple architecture, fast decoding ability, and high compression rate. Full-search VQ can typically be used to determine optimal codewords, but requires considerable computational time and resources. In this study, a hybrid VQ combining a tree structure and a Voronoi diagram is proposed to improve VQ efficiency. To efficiently reduce the search space, a tree structure integrated with principal component analysis is proposed, to rapidly determine an initial codeword in low-dimensional space. To increase accuracy, a Voronoi diagram is applied to precisely enlarge the search space by modeling relations between each codeword. This enables an optimal codeword to be efficiently identified by rippling an optimal neighbor from parts of neighboring Voronoi regions. The experimental results demonstrated that the proposed approach improved VQ performance, outperforming other approaches. The proposed approach also satisfies the requirements of handheld device application, namely, the use of limited memory and network bandwidth, when a suitable number of dimensions in principal component analysis is selected.