In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LEG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codcbook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks. © Springer-Verlag 2004.
CITATION STYLE
Kim, K. B., & Pandya, A. S. (2004). Color image vector quantization using an enhanced self-organizing neural network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3314, 1121–1126. https://doi.org/10.1007/978-3-540-30497-5_172
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