基于快速方向预测的高分辨率遥感影像压缩

张立保; 丘兵昌

doi:10.3788/OPE.20132108.2095

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基于快速方向预测的高分辨率遥感影像压缩

信息科学 | 更新时间：2020-08-12

- 基于快速方向预测的高分辨率遥感影像压缩
- Remote sensing image compression based on fast direction prediction
- 光学精密工程 2013年21卷第8期页码：2095-2102
- 作者机构：
  
  1. 北京师范大学信息科学与技术学院北京,100875
  2. 北京师范大学遥感科学国家重点实验室北京,100875
- 作者简介：
- 基金信息：
  
  基于ADL-IWT与人眼视觉关注模型的高空间分辨率遥感图像分级压缩();基于不同兴趣度的任意形状多感兴趣区图像编码方法研究()
- DOI：10.3788/OPE.20132108.2095
  中图分类号： TP391;TP752
- 收稿日期：2013-03-10，
  
  修回日期：2013-04-13，
  
  网络出版日期：2013-08-20，
  
  纸质出版日期：2013-08-15
- 稿件说明：
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张立保丘兵昌. 基于快速方向预测的高分辨率遥感影像压缩[J]. 光学精密工程, 2013,21(8): 2095-2102

ZHANG Li-bao QIU Bing-chang. Remote sensing image compression based on fast direction prediction[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2095-2102
张立保丘兵昌. 基于快速方向预测的高分辨率遥感影像压缩[J]. 光学精密工程, 2013,21(8): 2095-2102 DOI： 10.3788/OPE.20132108.2095.

ZHANG Li-bao QIU Bing-chang. Remote sensing image compression based on fast direction prediction[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2095-2102 DOI： 10.3788/OPE.20132108.2095.

摘要

针对传统的自适应方向提升小波变换（ADL-DWT)算法在高分辨率遥感影像压缩中计算复杂度过高的问题，提出一种新的基于方向预测的提升小波变换（DP-LWT）算法，实现了高分辨率遥感影像的快速、高效压缩。新算法首先将高分辨率遥感影像分为若干不重叠子块，然后采用梯度算子快速预测遥感影像中每个图像块的最佳提升方向，并沿着最佳预测方向插值完成方向提升小波变换，最后进行SPIHT编码。实验结果表明，新算法有效削弱了遥感影像各子带中非水平与非垂直方向的高频系数；与传统自适应方向提升小波变换相比，在重建高分辨率遥感影像峰值信噪比基本相同的情况下，有效减少了小波变换中方向预测的计算复杂度。

Abstract

As traditional Adaptive Direction Lifting based-Discrete Wavelet Transform（ADL-DWT) has higher computational complexity in the compression of high-resolution remote sensing images

this paper proposes a new lifting wavelet transform scheme based on Direction Prediction called DP-LWT to implement the fast and efficient compression of high-resolution remote sensing images. The new algorithm first divides a high-resolution remote sensing image into a number of non-overlapping sub-blocks. Then

the gradient operator is used to predict the best lifting direction of every sub-block in the remote sensing image quickly

and completes the direction lifting wavelet transform by the interpolation along the best lifting direction. Finally

the remote sensing image is coded by SPIHT. The experimental results show that the new algorithm effectively weakens the high-frequency coefficients on the non-horizontal and non-vertical directions of every image subband. Compared with the traditional ADL

the DP-LWT can effectively reduce the time computational complexity of directional prediction in lifting wavelet transform

and keeps the Peak Signal to Noise Ratio (PSNR) of the reconstructed high-resolution remote sensing image to be the same as that of the ADL basically.

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references

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