한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)
- 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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- Pages.81-84
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- 2000
신경망이 벡터양자화와 프랙탈 혼합시스템에 미치는 영향
A Study on the Hybrid Fractal clustering Algorithm with SOFM vector Quantizer
초록
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.
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