LISA: Image Compression Scheme Based on an Asymmetric Hierarchical Self-Organizing Map

Authors:
Cheng-Fa Tsai;Yu-Jiun Lin
Affiliations:
Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan 91201;Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan 91201
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

Citing 4
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Abstract

A Kohonen network, also called Self-Organizing Map (SOM), is a competitive learning network, and is appropriate for solving an image compression problem owing to its ability to generate high-quality compressed images. However, SOM has a large computation cost, making it impractical due to a lengthy training process. Hence, the Hierarchical Self-Organizing Map (HSOM) had been presented and found to reduce computation cost. Although a hierarchical architecture speeds up SOM, HSOM is still not practical enough because of a high compression cost. Therefore, this investigation employs a hybrid scheme to increase the efficiency and effectiveness of HSOM. Simulation results reveal that the proposed algorithm is much more efficient and effective than other algorithms, such as LBG, SOM, and HSOM.