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Wan Liu

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Proceedings Article | 3 January 2020 Paper

Perceptual spatial-temporal video compressive sensing network

Wan Liu, Xuemei Xie, Zhifu Zhao, Guangming Shi

Proceedings Volume 11373, 113731M (2020) https://doi.org/10.1117/12.2558039

KEYWORDS: Video, Compressed sensing, Feature extraction, Convolution, Neural networks

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Deep neural networks have been applied to video compressive sensing (VCS) task recently. The existing DNN-based VCS methods compress and reconstruct the scene video only in space or time dimensions, which ignores the spatial-temporal correlation of the video. And they generally utilize pixel-wise loss as the loss function, which causes the results to be over-smoothed. In this paper, we propose a perceptual spatial-temporal VCS network. The spatial-temporal VCS network, which compresses and recovers the video in both space and time dimensions, can preserve the spatial-temporal correlation of the video. Besides, we refine the perceptual loss by selecting specific feature-wise loss terms and adding a pixel-wise loss term. The refined perceptual loss can guide the spatial-temporal network to retain more textures and structures. Experimental results show the proposed method can achieve better visual effect with less recovery time than the state-of-the-art.

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