• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >Hybrid Prediction and Fractal Hyperspectral Image Compression
【24h】

Hybrid Prediction and Fractal Hyperspectral Image Compression

机译：混合预测与分形高光谱图像压缩

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.
机译：高光谱图像的数据大小太大，无法存储和传输，已经成为限制其应用的瓶颈。因此有必要研究一种高效的高光谱图像压缩方法。预测编码易于实现，并且已经在高光谱图像压缩领域中进行了广泛的研究。分形编码具有压缩率高，分辨率独立性强，解码速度快的优点，但在高光谱图像压缩领域的应用并不普遍。在本文中，我们提出了一种基于混合预测和分形的高光谱图像压缩新算法。对第一频带执行带内预测，并通过改进的分形编码算法对所有其余频带进行编码。该算法可以有效利用高光谱图像中的光谱相关性，因为每个距离块都由与该距离块相同大小的相邻频带中的域块来近似。实验结果表明，该算法在低比特率下具有很好的性能。与其他算法相比，编码复杂度较低，解码质量有了很大提高，PSNR可以提高约5 dB至10 dB。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|950357.1-950357.10|共10页
  • 作者

    Zhu Shiping; Zhao Dongyu; Wang Fengchao;

  • 作者单位

    Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Dept Measurement Control & Informat Technol, Beijing 100191, Peoples R China.;

    Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Dept Measurement Control & Informat Technol, Beijing 100191, Peoples R China.;

    CSR Qingdao Sifang Locomot & Rolling Stock Co Ltd, Qingdao 266111, Peoples R China.;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号