A new compressive sensing video coding framework based on Gaussian mixture model

被引:4
作者
Li, Xiangwei [1 ,2 ]
Lan, Xuguang [1 ]
Yang, Meng [1 ]
Xue, Jianru [1 ]
Zheng, Nanning [1 ]
机构
[1] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China
[2] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710049, Shaanxi, Peoples R China
关键词
Compressive sensing video; Gaussian mixture model; Lossy compression; Video coding; Product vector quantizer; COMPUTATION; STANDARD;
D O I
10.1016/j.image.2017.03.009
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we specifically design an efficient compressive sensing video (CSV) coding framework for the CSV system, by considering the distribution characteristics of the CSV frame. To explore the spatial redundancy of the CSV, the CSV frame is first divided into blocks and each block is modeled by a Gaussian mixture model (GMM), and then it is compressed by a product vector quantization. We further explore the temporal redundancy of the CSV by encoding the adjacent CSV frames by the differential pulse code modulation technique and the arithmetic encoding technique. Experiment results show that the proposed CSV coding solution maintains low coding complexity, which is required by the CSV system. Meanwhile, it achieves significant BD-PSNR improvement by about 7.13-11.41 dB (or equivalently 51.23-66.96% bitrate savings) compared with four existing video coding solutions, which also have low computational complexity and suit for the CSV system.
引用
收藏
页码:66 / 79
页数:14
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