Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video
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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。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.
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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 
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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
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