一种视频压缩感知中两级多假设重构及实现方法

欧伟枫; 杨春玲; 戴超

doi:10.11999/JEIT161142

一种视频压缩感知中两级多假设重构及实现方法

doi: 10.11999/JEIT161142

2.
(华南理工大学电子与信息学院广州 510640) ②(华为技术有限公司深圳 518129)

基金项目:

国家自然科学基金(61471173)，广东省自然科学基金(2016A030313455)

计量
- 文章访问数: 1217
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- 被引次数: 0
出版历程
- 收稿日期: 2016-10-26
- 修回日期: 2017-03-21
- 刊出日期: 2017-07-19

A Two-stage Multi-hypothesis Reconstruction and Two Implementation Schemes for Compressed Video Sensing

2.
(School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China)

Funds:

The National Natural　Science Foundation of China (61471173), The Natural Science Foundation of Guangdong Province (2016A030313455)

摘要

摘要: 视频压缩感知在采集端资源受限的视频采集应用场景有重要研究意义。重构算法是视频压缩感知的关键技术，基于多假设预测的预测-残差重构框架具有良好的重构性能。但现有的多假设预测算法大多在观测域提出，这种预测方法由于受到不重叠分块的限制，造成了预测帧的块效应，降低了重构质量。针对此问题，该文将像素域多假设预测与观测域多假设预测相结合，提出两级多假设重构思想(2sMHR)，并设计了基于图像组(Gw_2sMHR)和基于帧(Fw_2sMHR)的两种实现方法。仿真结果表明，所提2sMHR重构算法能有效减小块效应，相比于现有最好的多假设预测算法具有更低的时间复杂度和更高的视频重构质量。
- 视频压缩感知 /
- 重构 /
- 预测 /
- 多假设 /
- 稀疏
Abstract: Compressed Video Sensing (CVS) has great significance to the scenarios with a resource-deprived video acquisition side. Reconstruction algorithm is the key technique in compressed video sensing. The Multi-Hypothesis (MH) prediction based prediction-residual reconstruction framework has good reconstruction performance. However, most of the existing multi-hypothesis prediction algorithms are proposed in measurement domain, which cause block artifacts in the predicted frames and decrease reconstruction accuracy due to the restriction of non-overlapping block partitioning. To address this issue, this paper proposes a two-stage Multi-Hypothesis Reconstruction (2sMHR) idea by incorporating the measurement-domain MH prediction with pixel-domain MH prediction. Two implementation schemes, GOP-wise (Gw) and Frame-wise (Fw) scheme, are designed for the 2sMHR. Simulation results show that the proposed 2sMHR algorithm can effectively reduce block artifacts and obtain higher video reconstruction accuracy while requiring lower computational complexity than the state-of- the-art CVS prediction methods.
- Compressed Video Sensing (CVS) /
- Reconstruction /
- Prediction /
- Multi-Hypothesis (MH) /
- Sparsity

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参考文献(24)

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