An Experimental Short Review on Color Image Quantization
Color image quantization corresponds to the process of reducing the number of color in digital images.In the literature, color image quantization is regarded as one of the most important technique in image processing due to various applications in real-world scenario. From the literature, it is evident that the clustering algorithms are widely adopted in color quantization approach.In this paper, we provide a short review on some of the techniques employed for color quantization. In this work, we considered the following approachessuch as (a) k-Means Algorithm, (b) Fuzzy c-Means Clustering (FCM), and (c) Self-Organizing Map Neural Network (d)Median Cut Algorithm(MC) and analyzed their performance on color quantization. To analyze the clustering task, RGB color-coding is employed. We have employed mean square error (MSE) as the performance indicator to evaluate the performance of color quantization methods.The experimental results have illustrated that all these techniques in comparison are able to find out the significant color in an image and have presented image with less number of color.
Keywords - K-Means, Fuzzy c-Means, Self-Organizing Map, Median Cut, Color Quantization, Image Processing