The increase of the need for image storage and transmission in computer systems has grown the importance of signal and image compression algorithms.The approach involving vector quantization (VQ) relies on designing a finite set of codes which will substitute the original signal during transmission with a minimal of distortion. Algorithms such as LGB and SOM work in an unsupervised manner toward finding a good codebook for a given training data. However, the number of code vectors (N) needed for VQ increases with the vector dimension, and full-search algorithms such as LGB and SOM can lead to large training and coding times. An alternative for reducing the computational complexity is the use of a tree-structured vector quantization algorithm. This paper presents an application of an hierarchical SOM for image compression in which reduces the search complexity from O(N) to O(log N), enabling a faster training and image coding. Results are given for conventional SOM, LBG and HSOM, showing the advantage of the proposed method.
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