Modified Forward Only Counterpropogation Network (MFOCPN) for Improved Color Quantization by Entropy based Sub-clustering

Reduction of the image colors, which is also called color quantization (CQ), has been the focus of recent research interest. It is as an integral part of various digital image related areas such as compression, segmentation etc.. Neural networks play a significant role in either assisting conventional color quantization techniques or providing standalone solutions for color quantization. In the present work three new algorithms have been proposed using modified forward only counterpropogation network (MFOCPN). These algorithms introduce, 1) sub-clustering in Kohonen layer for enhancing the clustering process, 2) a new entropy metric based initialization of the Kohonen layer for efficient color-map design and faster convergence of network. Further, the two approaches are merged, to yield third algorithm to achieve better results. The proposed algorithms have been tested on standard test image.