应用KN网加速的分形图像压缩方法
Study on fractal image compression method speeded up with KNN
随着多媒体技术的广泛应用，图像压缩技术得到了飞速发展，许多学者采用多种方法对其进行研究。分形编码技术以其解码迅速的特点逐渐获得关注，但其编码时间较长。因此，该领域的许多研究都是针对加速编码算法的。本文提出了一种结合KNN网的分形编码算法，可大幅减少编码过程的计算量。实验结果表明，本文提出的方法与全搜索相比，在几乎不影响压缩率和图像质量的情况下，可大大加速图像编码过程。
Fractal coding allows fast decoding but suffers from long encoding times. Because of that, most of the research efforts carried on this field are focused on speeding up the encoding algorithm. We present in this paper a new method that significantly reduces the computational load of fractal image coding. Experimental results and analysis show that proposed method can greatly speed up image coding process with a little worse image quality and compression ratio compared with the exhaustive search method.
王焱、马勇
计算技术、计算机技术
图像压缩 分形图像压缩 Kohonen神经网络
Image compression Fractal image compression Kohonen neural nets
王焱,马勇.应用KN网加速的分形图像压缩方法[EB/OL].(2006-07-31)[2025-04-27].http://www.paper.edu.cn/releasepaper/content/200607-337.点此复制
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