Abstract: Facing the challenge of increasing data volume due to higher resolution of video,realizing high quality video reconstruction with lower sampling rate can reduce the consumption of communication resources and thus reduce the difficulty of deployment at the sampling end.However,the existing video compressed sensing methods cannot fully utilize the inter-frame correlation of the video,and the reconstruction quality of the video at low sampling rates needs to be further improved.With the introduction of deep learning technology,distributed video compression sensing based on deep learning provides new ideas for video compression sensing reconstruction.Therefore,this paper combines 3D deformable convolution with Transformer to construct CS3Dformer network,which utilizes the effectiveness of 3D deformable convolutional network in capturing local and spatio-temporal features of video and learns spatio-temporal features between video frames,and at the same time,utilizes the advantages of Transformer in capturing long-range dependency features,which compensates to some extent for the advantages of convolutional neural network method in capturing the non-local similarity of the defects of image,and better realize the modeling of the video.This method is an end-to-end video compression perception method,the experimental results on multiple datasets verify the effectiveness of the proposed method.

Key words: Compressive sensing, Video reconstruction, Deformable convolution, Transformer, Convolutional nerul network