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首页> 外文期刊>Japan journal of industrial and applied mathematics >Domain search using shrunken images for fractal image compression
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Domain search using shrunken images for fractal image compression

机译:使用缩小的图像进行分形图像压缩的域搜索

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摘要

In this paper, we propose a new way of limiting the number of candidates of domains by using the shrunken image for Voronoi-based fractal image compression. And we show the result of computer simulations and confirm the effects of the proposed method. The process of domain search is the most critical process of fractal image compression because it takes exorbitant time to perform it. In the process of domain search, we have to use the term of Sigma r(i), Sigma d(i), Sigma r(i)(2), Sigma d(i)(2) and Sigma r(i)d(i), where r(i) is the sum of pixels for the ith range and d(i) is same one for the corresponding domain. We can calculate these terms by using cumulations for the rectangular range, but for the Voronoi range, since the shape of a range is different from each other, we can not use the cumulations for calculating these terms. Therefore, it is necessary to limit the number of candidates of domains for finding the appropriate domain in order to reduce the time of compressing image.
机译:在本文中,我们提出了一种通过使用缩小的图像进行基于Voronoi的分形图像压缩来限制域候选者数量的新方法。并且我们展示了计算机仿真的结果并证实了所提出方法的效果。域搜索过程是分形图像压缩的最关键过程,因为它需要花费大量时间才能执行。在域搜索过程中,我们必须使用术语Sigma r(i),Sigma d(i),Sigma r(i)(2),Sigma d(i)(2)和Sigma r(i)d (i),其中r(i)是第i个范围的像素之和,而d(i)对于相应的域是相同的。我们可以使用矩形范围的累积量来计算这些项,但是对于Voronoi范围,由于范围的形状互不相同,因此我们不能使用累积量来计算这些项。因此,为了减少压缩图像的时间,必须限制用于寻找适当域的域候选的数量。

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