Proceedings of the IEEK Conference (대한전자공학회:학술대회논문집)
- 2000.11d
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- Pages.195-198
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- 2000
A Study on the Hybrid Fractal clustering Algorithm with SOFM vector Quantizer
벡터양자화기와 혼합된 프렉탈의 클러스터링 알고리즘에 대한 연구
Abstract
Fractal image compression can reduce the size of image data by contractive mapping of original image. The mapping is affine transformation to find the block(called range block) which is the most similar to the original image. Fractal is very efficient way to reduce the data size. However, it has high distortion rate and requires long encoding time. In this paper, we present the simulation result of fractal and VQ hybrid systems which use different clustering algorithms, normal and improved competitive learning SOFM. The simulation results showed that the VQ hybrid fractal using improved competitive learning SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.
Keywords