VARIANCE-CUT: A fast color quantization method based on hierarchical clustering
M. Emre Celebi, Quan Wen · 2013
Color quantization is an important operation with many applications in graphics and image processing. Clustering algorithms have been extensively applied to this problem. In this paper, we propose a simple yet effective color quantization method based on divisive hierarchical clustering. Our method utilizes the commonly used binary splitting strategy along with several carefully selected heuristics that ensure a good balance between effectiveness and efficiency. We also propose a slightly computationally expensive variant of this method that employs local optimization using the Lloyd-Max algorithm. Experiments on publicly available test images demonstrate that the proposed method outperforms some of the most popular quantizers in the literature.