运动补偿预测残差稀疏重构的分布式压缩视频传感

针对低复杂度视频编码需求，基于压缩传感(CS：Compressive Sensing)理论，提出了一种分布式压缩视频传感算法。低复杂度的编码器独立随机投影关键帧和CS帧，采集压缩视频数据；在解码端进行运动补偿预测以利用帧间相关性，对预测残差稀疏重构实现CS帧重建。仿真测试表明，与现有的三种压缩视频传感算法相比，所提算法重建的视频质量更好，适合无线视频监控及无线视频传感网络等应用。

Distributed Compressive Video Sensing via Sparse Recovery of Motion Compensated Prediction Residual

In order to meet the requirement for low-complexity video encoding, a distributed compressive video sensing algorithm based on the compressive sensing (CS) theory was proposed. The algorithm directly captured compressed video data at a low-complexity encoder by randomly projecting key frames and CS frames. However, a high-complexity decoder performed compressive sensing reconstruction of CS frames by exploiting inter-frame correlations via motion compensation prediction and utilizing sparse recovery of prediction residual. Simulation results show that, the proposed algorithm outperforms three known compressive video sensing algorithms and is a promising candidate for wireless visual surveillance and visual sensor networks.