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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...
This paper presents a new approach toward the design of optimised codebooks by vector quantisation (VQ). A strategy of fuzzy k-means reinforced learning (FRL) is proposed which exploits the advantages offered by fuzzy clustering algorithms, competitive learning and knowledge of training vector and codevector configurations. Reinforced learning, which is consisted of attractive factor and repulsive...
This paper presents an optimizer based on particle swarm optimization and LBG (PSO-LBG) for vector quantization in image coding. Three swarms, including two initial swarms and one elitist swarm whose particles are selected from two initial swarms respectively, are applied to find the global optimum. At each iteration of a swarm's updating process, particles perform the basic operations of PSO, but...
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