Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Sensors (Basel). 2022 Aug 12;22(16):6043. doi: 10.3390/s22166043.
We propose three methods for the color quantization of superpixel images. Prior to the application of each method, the target image is first segmented into a finite number of superpixels by grouping the pixels that are similar in color. The color of a superpixel is given by the arithmetic mean of the colors of all constituent pixels. Following this, the superpixels are quantized using common splitting or clustering methods, such as median cut, k-means, and fuzzy c-means. In this manner, a color palette is generated while the original pixel image undergoes color mapping. The effectiveness of each proposed superpixel method is validated via experimentation using different color images. We compare the proposed methods with state-of-the-art color quantization methods. The results show significantly decreased computation time along with high quality of the quantized images. However, a multi-index evaluation process shows that the image quality is slightly worse than that obtained via pixel methods.
我们提出了三种用于超像素图像的颜色量化方法。在应用每种方法之前，首先通过将颜色相似的像素分组将目标图像分割成有限数量的超像素。超像素的颜色由所有组成像素的颜色的算术平均值给出。之后，使用常见的分割或聚类方法（例如中值切割、k-均值和模糊 c-均值）对超像素进行量化。通过这种方式，在原始像素图像进行颜色映射的同时生成调色板。通过使用不同的彩色图像进行实验验证了每种所提出的超像素方法的有效性。我们将所提出的方法与最先进的颜色量化方法进行了比较。结果表明，计算时间明显减少，同时量化图像的质量也很高。然而，多指标评估过程表明，图像质量略逊于像素方法获得的图像质量。
