Color Quantization Using Coreset Sampling
German Valenzuela, M. Emre Celebi, Gerald Schaefer · 2018
Color quantization is an important operation with many applications in computer graphics and image processing and analysis. Clustering algorithms have been extensively applied to this problem. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much attention in the colour quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we propose a novel color quantization method based on the k-means algorithm. The proposed method utilizes adaptive initialization, deterministic sub-sampling and efficient coreset construction to attain high speed and high quality quantization. Experiments on a set of benchmark images demonstrate the proposed method to be significantly faster than k-means while delivering nearly identical results.