中文摘要：

提出了一种基于LUV颜色空间的彩色图像量化算法．运用PGF技术对原始图像进行平滑，在去噪的同时保持图像的边缘和细节；将PGF滤波系数作为先验的权重信息传递给矢量量化部分；利用凝聚聚类对多余颜色进行合并．结果显示，本文算法在主观评价和量化误差上明显优于经典的K均值聚类算法．

英文摘要：

An image color quantization algorithm base on peer group filtering （PGF） and vector quantization （VQ） is proposed. Firstly, PGF technology is used to filter image in LUV space, which smoothes image and maintains edges and details. And then, the local maximum of the 3D color histogram of the filtered image are chosen as the VQ codewords to make the image quantized. A quantization distortion function is defined, it takes visual characteristics into account. Split of cluster with the highest quantization distortion is iteratively performed until the requirement of total distortion is satisfied. Finally, agglomerative clustering can be applied to merge close clusters if further reduction of number of quantization colors is desired. The experiment shows that the objective and subjective quality of image produced by our algorithm were obviously better than that classical K-means algorithm.