一种基于压缩感知的二维几何信号压缩方法

杜卓明; 耿国华; 贺毅岳

doi:10.3724/SP.J.1004.2012.01841

一种基于压缩感知的二维几何信号压缩方法

doi: 10.3724/SP.J.1004.2012.01841

1.
西北大学信息科学与技术学院可视化技术研究所西安 710127;
2.
江苏技术师范学院计算机工程学院常州 213001

详细信息

通讯作者:
杜卓明

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- 被引次数: 0
出版历程
- 收稿日期: 2011-05-25
- 修回日期: 2012-06-29
- 刊出日期: 2012-11-20

A 2-D Geometric Signal Compression Method Based on Compressed Sensing

1.
Institute of Visualization Technology, School of Information Science and Technology, Northwest University, Xi'an 710127;
2.
School of Computer Engineering, Jiangsu Teachers University of Technology, Changzhou 213001

摘要

摘要: 本文给出的压缩方法属于谱压缩方法. 谱压缩方法是一种常用的二维轮廓线模型压缩方法. 文章从压缩感知的角度解释了谱压缩方法, 并提出了基于压缩感知的二维轮廓线模型压缩方法. 首先利用二维轮廓线模型 Laplace 算子的特征向量构造了一组基. 二维轮廓线模型的几何结构在这组基下可以被稀疏表达. 利用随机矩阵对二维轮廓线模型的几何结构抽样, 完成压缩. 恢复过程中, 通过最优化1-范数, 实现几何信号的恢复. 实验结果表明, 该方法压缩速度快, 比例高, 恢复效果好, 适合对大型数据以及远距离数据进行压缩.
- 压缩感知 /
- 几何信号 /
- 随机抽样 /
- 稀疏表达
Abstract: Spectral compression method is a commonly used compression method in the field of two-dimensional contour model compression. This paper explains the spectral compression method from the perspective of compressed sensing and provides a compression method of two-dimensional contour model based on compressed sensing. Constructing a basis using Laplace operator of the two-dimensional contour model, we get the sparse representation of the 2-D geometric signal based on this basis. We complete compressing the two-dimensional contour model by sampling the two-dimensional contour model geometry information based on a random matrix. In the recovery process, we reconstruct the 2-D geometric signal through optimizing 1-norm of the sparse signal. Experimental results show that the compression ratio of this method is high, the restore effect is good, and it is suitable for large-scale data compression.
- Compressed sensing /
- geometric signal /
- random sampling /
- sparse representation

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