教師資料查詢 | 類別: 期刊論文 | 教師: 余 繁 YU FUN (瀏覽個人網頁)

標題:Evolutionary Fuzzy Particle Swarm Optimization Vector Quantization Learning Scheme in Image Compression
學年95
學期1
出版(發表)日期2007/01/01
作品名稱Evolutionary Fuzzy Particle Swarm Optimization Vector Quantization Learning Scheme in Image Compression
作品名稱(其他語言)
著者Feng, Hsuan-ming; Chen, Ching-yi; 余繁; Ye, Fun
單位淡江大學電機工程學系
出版者Elsevier
著錄名稱、卷期、頁數Expert Systems with Applications 32(1), pp.213-222
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.
關鍵字Fuzzy inference analysis;Particle swarm optimization;Vector quantization;LBG algorithm;Image compression
語言英文
ISSN0957-4174
期刊性質國內
收錄於
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,電子版
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