现有的视频压缩感知( CVS) 多假设预测方法均以当前块在参考帧对应搜索范围内的所有搜索块为假设块，造成求解线性权值系数的计算复杂度过高和预测精度受限． 针
对该问题，文中提出了一种基于多参考帧的最优多假设预测视频压缩感知重构算法． 该算法首先从多个参考帧中选取出与当前块测量域绝对差值和( SAD) 最小的一部分搜索块作为当前块的最优假设块集，然后对假设块进行自适应线性加权，充分地挖掘视频帧间相关信息，提升了预测精度，同时降低了求解线性权值系数的计算复杂度; 最后对测量值进行帧间DPCM 量化，以提高视频压缩效率和率失真性能． 仿真实验表明，与现有的视频压缩感知重构算法相比，文中算法具有更高的视频重构质量．

The existing multi-hypothesis prediction methods for compressed video sensing ( CVS) select all possible blocks within the search space of reference frames as the hypotheses,which causes a high computation load in solving linear weighting coefficients and impairs prediction accuracy.To address this issue,a multi-reference framesbased optimal multi-hypothesis prediction algorithm for CVS reconstruction is proposed in this paper.In the algorithm,first,those search blocks which have the smallest sum of absolute differences ( SAD) from current block in measurement domain are selected from multi-reference frames as the optimal hypotheses of current block.Then,the hypotheses are weighted both linearly and adaptively to fully excavate the temporal correlation between video frames.Thus,the prediction accuracy is improved and the computation load in solving linear weighting coefficients is reduced.Finally,the compressed sensing measurements are quantized through the frame-based DPCM quantization to improve video compression efficiency and rate-distortion performance.Simulation results show that,in comparison with the existing CVS reconstruction algorithms,the proposed algorithm achieves higher video reconstruction quality.