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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46086

    題名: Evolutionary Fuzzy Particle Swarm Optimization Vector Quantization Learning Scheme in Image Compression
    作者: Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Fuzzy inference analysis;Particle swarm optimization;Vector quantization;LBG algorithm;Image compression
    日期: 2007-01-01
    上傳時間: 2010-08-10 10:58:57 (UTC+8)
    出版者: Elsevier
    摘要: This article develops an evolutional fuzzy particle swarm optimization (FPSO) learning algorithm to self extract the near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy particle swarm optimization vector quantization (FPSOVQ) learning schemes, combined advantages of the adaptive fuzzy inference method (FIM), the simple VQ concept and the efficient particle swarm optimization (PSO), are considered at the same time to automatically create near optimum codebook to achieve the application of image compression. The FIM is known as a soft decision to measure the relational grade for a given sequence. In our research, the FIM is applied to determine the similar grade between the codebook and the original image patterns. In spite of popular usage of Linde–Buzo–Grey (LBG) algorithm, the powerful evolutional PSO learning algorithm is taken to optimize the fuzzy inference system, which is used to extract appropriate codebooks for compressing several input testing grey-level images. The proposed FPSOVQ learning scheme compared with LBG based VQ learning method is presented to demonstrate its great result in several real image compression examples.
    關聯: Expert Systems with Applications 32(1), pp.213-222
    DOI: 10.1016/j.eswa.2005.11.012
    顯示於類別:[電機工程學系暨研究所] 期刊論文


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