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引用次数: 0
摘要
彩色图像量化是图像压缩领域中应用最广泛的技术。DBSCAN是一种基于密度的数据聚类技术。然而，DBSCAN被广泛用于数据聚类，但由于其相关的一些问题，在彩色图像量化方面不太流行。与DBSCAN相关的一个问题是，当对整个图像数据使用时，它会变得昂贵，而且噪声点也会被取消映射。本文提出了一种新的彩色图像量化方案，克服了这些问题。我们提出的算法是GDBSCAN (Grid Based DBSCAN)，我们首先将图像数据分解成网格，然后在每个网格上应用DBSCAN算法
Color image quantization is the most widely used techniques in the field of image compression. DBSCAN is a density based data clustering technique. However DBSCAN is widely used for data clustering but not very popular for color image quantization due to some of issues associated with it. One of the problems associated with DBSCAN is that it becomes expensive when used on whole image data and also the noise points been unmapped. In this paper we are proposing a new color image quantization scheme which overcomes these problems. Our proposed algorithm is GDBSCAN (Grid Based DBSCAN) where we first decompose the image data in grids and then apply DBSCAN algorithm on each grids
期刊介绍：
International Journal of Communication Networks and Information Security (IJCNIS) is a scholarly peer reviewed international scientific journal published three times (April, August, December) in a year, focusing on theories, methods, and applications in networks and information security. It provides a challenging forum for researchers, industrial professionals, engineers, managers, and policy makers working in the field to contribute and disseminate innovative new work on networks and information security.
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