Compressive sensing theory indicates that high-dimensional signal reconstruction can be obtained from far fewer measurements than those required by the Nyquist-Shannon sampling theorem

and compressive sensing has a great potential in video signal sensing. The existing reconstruction algorithms utilize the multihypothesis prediction to derive the residual model. A large number of studies adopt the method based on the least mean square error to select multiple hypothesis matching patehes and reconstruct the video

while the maximization of the structural similarity is not considered in these algorithms

and there is much room for improvement in the overall quality effect of image reconstruction. Therefore

a three-phase video compressive sensing reconstruction algorithm is proposed in this paper on the basis of dynamic multi-pattern matching

in which the first phase reconstructs each frame independently

the second phase dynamically selects the hypothesis patches from the reference frames and reconstructs the frames

and the final reconstruction result is obtained in the third phase with the best structural similarity. Experimental results demonstrate that compared with the state-of-the-art algorithm

the proposed algorithm could obtain better prediction accuracy and reconstruction quality for video compressive sensing.