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    jsp.display-item.identifier=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/987654321/60943

    题名: Heuristic particle swarm optimization learning-based image compression system
    作者: Feng, Hsuan-Ming;Chen, Ching-Yi;Ye, Fun
    贡献者: 淡江大學電機工程學系
    日期: 2008-07
    上传时间: 2011-10-15 01:07:54 (UTC+8)
    出版者: Philadelphia: Taylor & Francis Inc.
    摘要: Traditional LBG algorithm is a pure iterative optimization procedure to achieve the vector quantization (VQ) codebook, where an initial codebook is continually refined at every iteration to reduce the distortion between code-vectors and a given training data set. However, such interactive type learning algorithms will easily direct final results converging toward the local optimization while the high quality of the initial codebook is not available. In this article, an efficient heuristic-based learning method, called novel particle swarm optimization (NPSO), is proposed to design the proper codebook of VQ scheme that can develop the image compression system. To improve the performance of the basic PSO, the centroid updating machine applies the one step-size gradient descent learning step in the heuristic learning procedure. Additionally, the presented NPSO with advantages of the centroid updating machine is proposed to quickly achieve the near-optimal reconstructive image. For demonstrating the proposed NPSO learning scheme, the image with several horizontal grey bars is first applied to present the efficiency of the NPSO learning mechanism. LBG and NPSO learning methods are also applied to test the reconstructing performance in several type images “Lena,” “Airplane,” “Cameraman”, and “peppers.” In our experiments, the NPSO learning algorithm provides the higher performance than conventional LBG methods in the application of building image compression system.
    關聯: Cybernetics and Systems: An International Journal 39(5), pp.520-537
    DOI: 10.1080/01969720802069906
    显示于类别:[電機工程學系暨研究所] 期刊論文


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