English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58323/91877 (63%)
Visitors : 14314177      Online Users : 58
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46086


    Title: Evolutionary Fuzzy Particle Swarm Optimization Vector Quantization Learning Scheme in Image Compression
    Authors: Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
    Contributors: 淡江大學電機工程學系
    Keywords: Fuzzy inference analysis;Particle swarm optimization;Vector quantization;LBG algorithm;Image compression
    Date: 2007-01-01
    Issue Date: 2010-08-10 10:58:57 (UTC+8)
    Publisher: Elsevier
    Abstract: 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.
    Relation: Expert Systems with Applications 32(1), pp.213-222
    DOI: 10.1016/j.eswa.2005.11.012
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

    Files in This Item:

    File SizeFormat
    0957-4174_32(1)p213-222.pdf712KbAdobe PDF268View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback