Color quantization is a critical task, frequently involved in image processing that reduces the number of distinct colors used in an image while retaining as much of the original representation capabilities. The key aspect here is to find the optimal palette and evaluate against unprocessed target images. The purpose of this paper is to compare the effectiveness of three well known unsupervised vector quantization algorithms (Neural Gas, Growing Neural Gas and Instantaneous Topological Map) in the field of color abstraction. Evaluation data for L*a*b* and L*u*v* uniform color spaces and a number of quality indices, exhibiting the performance in terms of overall quality, are presented.
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机译：颜色量化是一项至关重要的任务，通常涉及图像处理，可减少图像中使用的不同颜色的数量，同时保留尽可能多的原始表示功能。此处的关键方面是找到最佳的调色板并针对未处理的目标图像进行评估。本文的目的是比较颜色提取领域中三种众所周知的无监督矢量量化算法（神经气体，生长神经气体和瞬时拓扑图）的有效性。给出了L * a * b *和L * u * v *均匀色彩空间的评估数据以及许多质量指标，这些指标显示出整体质量方面的性能。
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