LazySOM: Image Compression Using an Enhanced 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:
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Year:
2009

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Abstract

A self-organizing map (SOM), i.e. a congenital clustering algorithm, has a high compression ratio and produces high-quality reconstructed images, making it very suitable for generating image compression codebooks. However, SOMs incur heavy computation particularly when using large numbers of training samples. Thus, to speed up training, this investigation presents an enhanced SOM (named LazySOM) involving a hybrid algorithm combining LBG, SOM and Fast SOM. The proposed algorithm has a low computation cost, enabling the use of SOM with large numbers of training patterns. Simulations are performed to measure two indicators, PSNR and time cost, of the proposed LazySOM.