基于双边信息的残差分布式压缩视频感知

陈建; 苏凯雄; 王卫星; 兰诚栋

doi:10.3724/SP.J.1004.2014.02316

基于双边信息的残差分布式压缩视频感知

doi: 10.3724/SP.J.1004.2014.02316

1.
福州大学物理与信息工程学院福州350116

基金项目:

Supported by National Natural Science Foundation of China (61170147), Major Cooperation Project of Production and College in Fujian Province (2012H61010016), and Natural Science Foundation of Fujian Province (2013J01234)

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出版历程
- 收稿日期: 2013-09-24
- 修回日期: 2014-04-21
- 刊出日期: 2014-10-20

Residual Distributed Compressive Video Sensing Based on Double Side Information

1.
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China

Funds:

More Information

Corresponding author: SU Kai-Xiong Professor at the College of Physics and Information Engineering, Fuzhou University. He received his master degree from University of Science and Technology of China in 1988. His research interest covers wireless communications, video broadcasting, and embedded systems. Corresponding author of this paper. E-mail: skx@fzu.edu.cn

摘要

摘要: 压缩感知(CS)是在低于奈奎斯特率条件下获取和重构稀疏信号的新兴技术,在图像和视频获取和处理中有巨大的发展潜力.为了有效提高被测信号的稀疏性和重构效率,本文提出一种基于双边信息的残差分布式压缩视频感知(RDCVS-DSI)编解码模型.该模型利用了图像自身的频域特性和邻近帧之间的相关性,以低质量的视频帧作为编解码的第一边信息,解码端利用关键帧运动估计和运动补偿技术生成非关键帧的第二边信息.通过性能分析和仿真测试表明,该RDCVS-DSI模型能够在较低复杂度条件下,高保真地重建视频序列.与以前的压缩视频感知工作对比,重构帧的平均峰值性噪比达到1-5dB的增益,重构速度接近于复杂度最低的DCVS.
- 视频编码 /
- 压缩感知 /
- 分布式压缩视频编码 /
- 残差编码
Abstract: Compressed sensing (CS) is a novel technology to acquire and reconstruct sparse signals below the Nyquist rate. It has great potential in image and video acquisition and processing. To effectively improve the sparsity of signal being measured and reconstructing efficiency, an encoding and decoding model of residual distributed compressive video sensing based on double side information (RDCVS-DSI) is proposed in this paper. Exploiting the characteristics of image itself in the frequency domain and the correlation between successive frames, the model regards the video frame in low quality as the first side information in the process of coding, and generates the second side information for the non-key frames using motion estimation and compensation technology at its decoding end. Performance analysis and simulation experiments show that the RDCVS-DSI model can rebuild the video sequence with high fidelity in the consumption of quite low complexity. About 1~5 dB gain in the average peak signal-to-noise ratio of the reconstructed frames is observed, and the speed is close to the least complex DCVS, when compared with prior works on compressive video sensing.
- Video coding /
- compressed sensing /
- distributed compressive video sensing /
- residual coding

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