| Fast adaptive wavelet for remote sensing image compression |
| |
| 作者姓名： | Bo Li Run-Hai Jiao and Yuan-Cheng Li |
| |
| 作者单位： | Digital Media Laboratory School of Computer Science and Engineering Beihang University，Beijing 100083，China State Key Laboratory of Virtual Reality Technologies，Beihang University，Beijing 100083，China，Digital Media Laboratory School of Computer Science and Engineering，Beihang University，Beijing 100083，China，Digital Media Laboratory School of Computer Science and Engineering，Beihang University，Beijing 100083，China |
| |
| 基金项目： | Supported bY the National Natural Science Foundation of China under Grant No.60573150，National Defense Basic Research Foundation,the Program for New Century Excellent Talents in Universities and ERIPKU. |
| |
| 摘 要： | Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.
|
| 关 键 词： | 小波结构 遥感结构 图象压缩 图象分类 |
| 修稿时间： | 2007-01-15 |
Fast Adaptive Wavelet for Remote Sensing Image Compression |
| |
| Bo Li,Run-Hai Jiao,and Yuan-Cheng Li.Fast adaptive wavelet for remote sensing image compression[J].Journal of Computer Science and Technology,2007,22(5):770-778. |
| |
| Authors: | Bo Li Run-Hai Jiao Yuan-Cheng Li |
| |
| Affiliation: | 1.Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing 100083, China ;2.State Key Laboratory of Virtual Reality Technologies, Beihang University, Beijing 100083, China |
| |
| Abstract: | Remote sensing images are hard to achieve high compression ratio because of their rich texture.By analyzing the influence of wavelet properties on image compression,this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters.The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function.In addition,in order to resolve the computation complexity problem of online construction,according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm (FAWS).Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7. |
| |
| Keywords: | wavelet construction remote sensing image image compression energy compaction image classification fast adaptive wavelet selection |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
| 点击此处可从《计算机科学技术学报》浏览原始摘要信息 |
|
点击此处可从《计算机科学技术学报》下载全文 |
