聚类分析
调色板(绘画)
量化(信号处理)
算法
计算机科学
树冠聚类算法
k均值聚类
图像(数学)
失真(音乐)
人工智能
相关聚类
数学
带宽(计算)
计算机网络
操作系统
放大器
标识
DOI:10.1179/174313107x176298
摘要
AbstractAbstractThe k-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major problem is that a great deal of computational cost is consumed. To accelerate the k-means clustering algorithm, two test conditions are employed in the proposed algorithm. From the experimental results, it is found that the proposed algorithm significantly cuts down the computational cost of the k-means clustering algorithm without incurring any extra distortion.Keywords: COLOUR IMAGE QUANTIZATIONK-MEANS CLUSTERING ALGORITHMVECTOR QUANTIZATIONLBG ALGORITHM
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