TY - JOUR
AU - 
AB - symmetry Article Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms Shu-Chien Huang Department of Computer Science, National Pingtung University, Pingtung City, Pingtung County 90003, Taiwan; schuang@mail.nptu.edu.tw Received: 20 June 2020; Accepted: 23 July 2020; Published: 25 July 2020 Abstract: Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is eciently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption. Keywords: color image 
TI - Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms
JF - Symmetry
DO - 10.3390/sym12081222
DA - 2020-07-25
UR - https://www.deepdyve.com/lp/unpaywall/color-image-quantization-based-on-the-artificial-bee-colony-and-ldYSPxYPfM
DP - DeepDyve
ER -