In order to reduce the large data volumes in video processing

we combine the first-order Auto Regressive Moving Average (ARMA)model video model with compressed sensing theory

and propose a compressed sensing video model

which is based on the first-order ARMA. The main idea is making full use of video sparsity and frame coherence under the theoretical framework of compressed sensing

and dividing the video into a static part and a dynamic part. The new model gets the key parameters through simultaneous sampling and separate processing. Moreover

we discuss the construction conditions of the model and provide concrete guidelines on how to use this new model with provable performance. We present experimental evidence that

within our framework

the data volume can be reduced largely and reconstructed video shows a robust result even with compression rates at a ratio of 100 to 200. Combining with the compressed sensing and linear prediction technology

we propose a new video acquisition model

which static and dynamic parts of video can process respectively.Additionally

we give the conditions of using this model. Experiments show that the model has a well compression effect while facing smooth video.