Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video

Jianming WANG; Jianhua CHEN

doi:10.11999/JEIT190750

Volume 42 Issue 12

Dec. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(12): 3021-3028

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

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Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video

doi: 10.11999/JEIT190750

Jianming WANG,
Jianhua CHEN^,

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

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

Received Date: 2019-09-29
Rev Recd Date: 2020-09-27

Available Online: 2020-09-29

Publish Date: 2020-12-08

Abstract

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

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References(23)

References

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