Speedup of Color Palette Indexing in Self–Organization of Kohonen Feature Map

Abstract

Based on the self–organization of Kohonen feature map (SOFM), recently, Pei et al. presented an efficient color palette indexing method to construct a color table for compression. Taking their palette indexing method as a representative, this paper presents two new strategies, the pruning–based search strategy and the lookup table (LUT)–based update strategy, to speed up the learning process in the SOFM. Based on four typical testing images, experimental results demonstrate that our proposed two strategies have 35% execution–time improvement ratio in average. In fact, our proposed two strategies could be used to speed up the other SOFM–based learning processes in different applications.