Color quantization by pairwise clustering using a reduced graph

The aim of this work is to investigate an improvement in the performance of the pairwise clustering algorithm (PCA) for color quantization of images, that algorithm uses a local error optimization strategy to generate near optimal quantization levels. We investigate the behavior of the accumulated error in the final images when, instead of computing distances between all pairs of colors, a reduced graph is used. We take advantage of a sorted vector of colors to reduce the number of neighbors considered by each node of the graph of distances.