A Color Image Quantization Algorithm Based on Particle Swarm Optimization

A color image quantization algorithm based on Particle Swarm Optimization (PSO) is developed in this paper. PSO is a population-based optimization algorithm modeled after the simulation of social behavior of bird flocks and follows similar steps as evolutionary algorithms to find near-optimal solutions. The proposed algorithm randomly initializes each particle in the swarm to contain K centroids (i. e. color triplets). The K-means clustering algorithm is then applied to each particle at a user-specified probability to refine the chosen centroids. Each pixel is then assigned to the cluster with the closest centroid. The PSO is then applied to refine the centroids obtained from the K-means algorithm. The proposed algorithm is then applied to commonly used images. It is shown from the conducted experiments that the proposed algorithm generally results in a significant improvement of image quality compared to other well-known approaches. The influence of different values of the algorithm control parameters is studied. Furthermore, the performance of different versions of PSO is also investigated.