In contrast with the Shannon sampling which requires large-scale samples

the compressive sampling has unique advantages in energy-efficient representation of video signals. When the sampling subrates of a keyframe and a non-keyframe are inconsistent

the existing interframe patch matching algorithms show unstable recovery quality under different subrate combinations. In order to fully utilize the temporal correlation between frames

a subrate-adaptive interframe patch matching algorithm was proposed for video compressive sensing reconstruction

where the differential interframe matching mechanism was performed according to the relative change between keyframe sampling subrate and non-keyframe sampling subrate for better adaptation of video bitstream generated from different subrate combinations. Firstly

the measurements of each frame were reconstructed to obtain the corresponding intraframe results respectively. Then

the adaptive interframe reconstruction was implemented according to the growth ratio of the keyframe sampling subrate to the non-keyframe sampling subrate. In the case of low growth ratio

the current frame selected the nearest keyframe and the non-keyframe in the same direction as its co-directional double reference frames for patch matching reconstruction. In the case of high growth ratio

the current frame gradually selected the reconstruction results of multiple reference frames for multi-stage patch matching reconstruction. Compared with the existing reconstruction algorithms

the proposed algorithm could achieve more stable reconstruction performance during the typical framework of video compressive sensing

and thus the recovery quality of video sequence was consistently improved.