A novel Genetic Particle-Pair Optimizer for Vector Quantization in image coding

This paper presents a novel genetic particle-pair optimizer (GPPO) for vector quantization of image coding. GPPO only applies a particle-pair that consists of two particles, which contributes to the relief of huge computation load in most existing vector quantization algorithms. GPPO combines the advantage both in genetic algorithms and particle swarm optimization, due to the use of genetic operators and particle operators at each generation. Experimental results have demonstrated that the quality of the codebook design optimized by GPPO is better than that optimized respectively by fuzzy K-means (FKM), fuzzy reinforcement learning vector quantization (FRLVQ, improved FRLVQ which uses fuzzy vector quantization (FVQ as post-process, called FRLVQ-FVQ, and particle-pair optimizer (PPO). GPPO provides a satisfactory solution to vector quantization, and shows a steady trend of improvement in the quality of codebook design. The dependence of the final codebook on the selection of the initial codebook is also reduced.