The existing approaches to reconstruct compressed video sensing achieve heavy quality fluctuation when reconstructing videos with different motion feature. To solve this problem

combing the merits of two CS (Compressed Sensing) methods: The clearly edges and fine details reconstruction of the dual sparsity representation and the effectively high frequency noise suppression of multi-hypothesis prediction

this paper proposes a video motion features based multi-hypothesis-dual-sparsity reconstruction algorithm (VF-MH-DSR) for compressed video sensing (CVS). The basic thinking of VF-MH-DSR is that adopting a corresponding MH-DSR method to each video group (GOP) based on their motion features.In our approach

we firstly develop a multi-hypothesis reconstruction algorithm based on multi-dimension reference frames in measurement domain (MD-MRF-MH) and a kind of setting scheme for optimal similar block.Then

multi-hypothesis reconstruction algorithm based on multi-dimension reference frames in pixel domain (PD-MRF-MH) and a double matching criterion to improve matching accuracy are proposed. Finally

we develop a strategy to determine the video motion feature and introduce the scheme of multi-hypothesis-dual-sparsity reconstruction. Simulation results show that the proposed VF-MH-DSR outperforms the existing state-of-art compressed video sensing reconstruction algorithms 2sMHR and SSIM-InterF-GSR by 1.98dB and 0.84dB respectively.