结合一阶自回归滑动平均和压缩感知的视频模型

Compressed sensing video model based on a first-order auto regressive moving average model

目的 针对视频处理中面临的采样数据量大及采样时间长的问题,把视频状态空间一阶自回归滑动平均模型和压缩感知模型相结合,提出了一种基于一阶自回归滑动平均的视频压缩感知模型.方法 主要思想是在压缩感知理论框架下,充分利用视频帧内稀疏性和帧间相关性,把视频分割成动态部分和静态部分同时采样但分别处理,利用凸优化等方法得到视频状态空间一阶自回归滑动平均模型的关键参数.结果 多组真实场景下的实验结果表明,该模型较大程度上降低了帧间冗余度和数据采集量,视频采集压缩比为100～200时,仍然能取得较好的重建效果.结论 结合压缩感知和线性预测技术,提出了一种新的视频获取模型,对视频的静态部分和动态部分分别处理,并给出了该模型使用的条件.实验结果表明,该模型对帧间变化不大的视频,具有良好的压缩效果.

Objective In order to reduce the large data volumes in video processing, we combine the first-order Auto Re- gressive 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. Method 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 dy- namic 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. Result We present experimental evidence that, within our framework, the data volume can be reduced large- ly and reconstructed video shows a robust result even with compression rates at a ratio of 100 to 200. Conclusion Combi- ning 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.