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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

王健明, 陈建华. 使用能量匹配的监控视频自适应速率压缩感知[J]. 电子与信息学报, 2020, 42(12): 3021-3028. doi: 10.11999/JEIT190750
引用本文: 王健明, 陈建华. 使用能量匹配的监控视频自适应速率压缩感知[J]. 电子与信息学报, 2020, 42(12): 3021-3028. doi: 10.11999/JEIT190750
Jianming WANG, Jianhua CHEN. Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3021-3028. doi: 10.11999/JEIT190750
Citation: Jianming WANG, Jianhua CHEN. Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3021-3028. 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
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

Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video

Funds: The National Natural Science Foundation of China (61861045)
  • 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  1  视频序列范例

    图  2  测试视频稀疏程度估计表现

    图  3  测试视频采样速率

    图  4  测试视频图像重建质量

    表  1  实验参数

    参数$\varSigma $$a$$b$$\tau $$r$
    视频序列Hall2.65161288600
    视频序列PETS2.45161288600
    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)
    Oracle0.204036.590.131740.02
    CDSAM方法0.229736.340.200139.53
    ARCS-CV方法0.213737.030.119139.07
    ARCS-EM方法0.223237.260.135040.26
    下载: 导出CSV

    表  3  采样运行时间对照表(ms)

    运行时间Hall视频T1Hall视频T2Hall视频T3Hall视频TPETS视频T1PETS视频T2PETS视频T3PETS视频T
    CDSAM方法7.48104.180111.666.7694.930101.69
    ARCS-CV方法957.260.142.99×1053.00×105526.560.111.44×1051.45×105
    ARCS-EM方法800.870.420801.29498.750.510499.26
    下载: 导出CSV
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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

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