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Fast Color Quantization by K-Means Clustering Combined with Image Sampling

Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
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Symmetry 2019, 11(8), 963; https://doi.org/10.3390/sym11080963
Received: 18 May 2019 / Revised: 10 July 2019 / Accepted: 17 July 2019 / Published: 1 August 2019
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Abstract

Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process. View Full-Text
Keywords: color image; color quantization; clustering; K-Means; sampling; image quality color image; color quantization; clustering; K-Means; sampling; image quality
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Frackiewicz, M.; Mandrella, A.; Palus, H. Fast Color Quantization by K-Means Clustering Combined with Image Sampling. Symmetry 2019, 11, 963.

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