多假设预测是视频压缩感知多假设预测残差重构算法的关键技术之一，但目前 的多假设预测算法对运动剧烈的视频序列依然存在计算复杂度高且质量不佳的缺陷，而 且由于观测值与真实信号是一对多的关系，只采用观测值的绝对误差和准则选择假设块 容易引入噪声，从而限制了重构质量． 针对这些问题，文中结合视频前/后景的运动特征， 提出了基于菱形快速搜索的双匹配区域多假设预测算法( MH-DS) ，即利用菱形快速搜索 方式确定当前解码块的前景/后景的运动矢量，获得两个最佳搜索窗，从中搜索多假设匹 配块组; 在匹配过程中，采用融合最小均方误差和最大匹配像素统计的块匹配准则，以得 到更相关的假设块． 仿真结果表明，基于菱形快速搜索的双匹配区域多假设算法能够有效 地降低重构端多假设预测过程的计算复杂度，与现有最优视频压缩感知预测－重构算法 相比，提升了预测精度和重构质量． 

 Multi-hypothesis prediction (MH) is a key technique in compressed video sensing (CVS) predictionresidual reconstruct-algorithm． Unfortunately，when dealing with fast moving sequences，high computational complexity and low prediction accuracy are unavoidable． Besides，MH in measurement domain just employs the sum of absolute difference (SAD) principle to select hypothesis blocks，which usually introduces noise in the prediction blocks and decreases the reconstruction quality for neglecting the oneto-many relationship between the given measurement and original signals． To address these issues，this paper takes advantage of the motion features in video and proposes a multi-hypothesis prediction scheme based on fast diamond search with two matching regions (MHDS)． The MH-DS uses the fast diamond search method to search in two different directions for two optimal matching regions，where hypothesis blocks are obtained． MH-DS reduces the computational complexity of the searching process and get more effective prediction information． Moreover，a new matching criterion integrating mean square error (MMSE) with maximum pixels counting (MPC) is proposed in MH-DS in order to get more relevant hypothesis blocks． Simulative results show that the proposed MH-DS reduce the computational complexity of prediction process at reconstruction side and obtain higher prediction accuracy and higher reconstruction quality than the stateofthe-art CVS prediction methods．