改进误差反向传播(BP)神经网络在图像压缩中的应用

李季

应用光学 ›› 2013, Vol. 34 ›› Issue (6) : 974-979.

应用光学 ›› 2013, Vol. 34 ›› Issue (6) : 974-979.

改进误差反向传播(BP)神经网络在图像压缩中的应用

  • 李季
作者信息 +

Image compression used improved error back-propagation neural network

  • LI Ji
Author information +
文章历史 +

摘要

针对空间遥感图像数据量剧增的问题,提出一种改进的BP神经网络图像压缩方法。该算法利用Levenberg-Marquart算法提高神经网络的收敛速度,利用算法提高神经网络的泛化能力。比较分析了新算法和标准BP算法对同一幅图像进行压缩的结果和性能误差函数。实验结果表明,实验结果表明,标准BP算法在图像压缩比为1/2时,均方误差(MSE)为343.3750;改进后的BP算法在图像压缩比为1/16时,MSE为69.5796,图像压缩比为1/8时,MSE为20.9561,图像压缩比为1/4时,MSE为5.5123。并且利用改进后的算法压缩图像的峰值信噪比均在30 dB~40 dB之间。改进算法已用于实际工程中,满足实际需求。

Abstract

To overcome the dramatically increasing data amount of space remote sensing image, an improved back-propagation(BP) neural network was put forward to compress it. The algorithm used the Levenberg-Marquart algorithm to improve the convergence speed of neural network and used algorithm to improve the generalization ability of neural network. We compared and analyzed the compression result and error performance function of the improved algorithm and the standard BP algorithm to the same image. The experimental results show that, when the image compression ratio is 1/2, the mean square error (MSE) of standard BP algorithm is 343.3750; for improved BP algorithm, the MSE is 69.5796 when the image compression ratio is 1/16, the MSE is 20.9561 when the image compression ratio is 1/8, and the MSE is 5.5123 when the ratio is 1/4. Moreover, the peak signal-to-noise ratio (PSNR) of the improved algorithm is always in the range from 30 dB to 40 dB. The improved algorithm has been applied in practical engineering, which meets the need of practical work.

关键词

图像压缩 / Levenberg-Marquart算法 / 空间遥感图像 / α算法 / BP神经网络

Key words

image compression / BP neural network / space remote sensing image / Levenberg-Marquart algorithm / α algorithm

引用本文

导出引用
李季. 改进误差反向传播(BP)神经网络在图像压缩中的应用. 应用光学. 2013, 34(6): 974-979
LI Ji. Image compression used improved error back-propagation neural network. Journal of Applied Optics. 2013, 34(6): 974-979

参考文献

[1]霍承富.超光谱遥感图像压缩技术研究[D].安徽:中国科技大学,2012.
HUO Cheng-fu. Research on hyperspectral remote sensing image compression technique[D]. Hefei:University of Science and Technology of China, 2012.(in Chinese)
[2]SAID A, PEARLMAN W A. An image multire solution representation for lossless and lossy compression[J]. IEEE Trans Image Processing, 1996,5(9):1303-1310.
[3]赵米阳,陈卫东,卢晓燕. 基于SPIHT的改进图像压缩算法[J]. 应用光学,2007,28(4):388-391.
ZHAO Mi-yang, CHEN Wei-dong, LU Xiao-yan. Improvement of image compressing algorithm based on SPIHT[J]. Journal of Applied Optics, 2007, 28(4): 388-391. (in Chinese with an English abstract)
[4]刘海英,李云松,吴成柯,等. 一种高重构质量低复杂度的高光谱图像压缩感知[J]. 西安电子科技大学学报,2011,38(3):37-41.
LIU Hai-ying, LI Yun-song, WU Cheng-ke, et al. Compressed hyperspectral image sensing based on inte

10

Accesses

0

Citation

Detail

段落导航
相关文章

/