使用能量匹配的监控视频自适应速率压缩感知

王健明; 陈建华

doi:10.11999/JEIT190750

使用能量匹配的监控视频自适应速率压缩感知

doi: 10.11999/JEIT190750

王健明,
陈建华^,

云南大学信息学院昆明 650504

基金项目: 国家自然科学基金(61861045)

详细信息

作者简介:
王健明：男，1984年生，博士生，研究方向为数据压缩

陈建华：男，1964年生，教授，博士生导师，研究方向为信息传输理论与应用

通讯作者:
陈建华　chenjh@ynu.edu.cn

中图分类号: TN911.73
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- 文章访问数: 844
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- 被引次数: 64
出版历程
- 收稿日期: 2019-09-29
- 修回日期: 2020-09-27
- 网络出版日期: 2020-09-29
- 刊出日期: 2020-12-08

Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video

Jianming WANG,
Jianhua CHEN^,

School of Information Science and Engineering, Yunnan University, Kunming 650504, China

Funds: The National Natural Science Foundation of China (61861045)

摘要

摘要: 获取信号稀疏度对压缩感知(CS)性能的提升有重大意义，但在采样端不进行完整信号数字化采集和存储的情况下，对信号稀疏度进行估计比较困难。现有方法在稀疏度估计性能和计算复杂度方面难以取得较好的平衡。针对采样端对信号特性未知的监控视频应用，该文提出一种新的使用能量匹配的自适应速率压缩感知方法(ARCS-EM)，通过观测一个恒定低速率的压缩感知观测结果来对当前帧实际稀疏度进行估计，然后根据估计结果决定当前帧应执行的压缩感知测量数，再进行补充测量得到当前帧的优化压缩感知采样结果。实验结果表明，该方法可以较好地适应视频中前景稀疏度的变化，为每帧图像分配适当的压缩感知测量速率，在不显著提高采样端计算复杂度的前提下，有效提高重建视频的质量。
- 图像信号处理 /
- 压缩感知 /
- 自适应速率采样 /
- 能量匹配 /
- 监控视频
Abstract: Signal sparsity is of great significance for the improvement of Compressive Sensing (CS) performance. However, it is difficult to estimate the sparsity when the whole signal is not captured and stored at the sampling side. Few existing mothed can achieve good balance in terms of the sparsity estimation performance and the computational complexity. For the monitoring video applications where the signal characteristics is unknown for sampling devices, a new Adaptive-Rate CS using Energy Matching (ARCS-EM) method is proposed. By observing the measurement results of a low-rate compressive sensing, the actual sparsity of the current frame is estimated and then the rate of measurement for the current frame is determined. Finally, supplementary measurements are performed to obtain the optimized compressive sensing result for the current frame. Experiment results show that the proposed method could allocate suitable measurement rate for each frame to adapt to the variation of sparsity in different frames. The quality of reconstructed videos is effectively improved without noticeably increasing computational complexity in the sampling side.
- Image signal processing /
- Compressive Sensing (CS) /
- Adaptive-rate sampling /
- Energy matching /
- Monitoring video

HTML全文

图 1 视频序列范例

下载: 全尺寸图片幻灯片

图 2 测试视频稀疏程度估计表现

下载: 全尺寸图片幻灯片

图 3 测试视频采样速率

下载: 全尺寸图片幻灯片

图 4 测试视频图像重建质量

下载: 全尺寸图片幻灯片

表 1 实验参数

参数	$\varSigma$	$a$	$b$	$\tau$	$r$
视频序列Hall	2.65	16	128	8	600
视频序列PETS	2.45	16	128	8	600

下载: 导出CSV

表 2 不同方法的自适压缩感知平均性能对比

实验结果	Hall视频平均压缩感知采样率	Hall视频平均峰值信噪比(dB)	PETS视频平均压缩感知采样率	PETS视频平均峰值信噪比(dB)
Oracle	0.2040	36.59	0.1317	40.02
CDSAM方法	0.2297	36.34	0.2001	39.53
ARCS-CV方法	0.2137	37.03	0.1191	39.07
ARCS-EM方法	0.2232	37.26	0.1350	40.26

下载: 导出CSV

表 3 采样运行时间对照表(ms)

运行时间	Hall视频T₁	Hall视频T₂	Hall视频T₃	Hall视频T	PETS视频T₁	PETS视频T₂	PETS视频T₃	PETS视频T
CDSAM方法	7.48	104.18	0	111.66	6.76	94.93	0	101.69
ARCS-CV方法	957.26	0.14	2.99×10⁵	3.00×10⁵	526.56	0.11	1.44×10⁵	1.45×10⁵
ARCS-EM方法	800.87	0.42	0	801.29	498.75	0.51	0	499.26

下载: 导出CSV

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