期刊論文
| 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 | en |
| ISSN | 0957-4174 |
| 期刊性質 | 國內 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46086 ) |