帧测量率自适应分配的分布式视频压缩感知

传统分布式视频压缩感知通常对所有的非关键帧采用相同的测量率进行测量，这种测量方式并未考虑到不同帧之间的相关性具有差异性，造成帧组的重构质量不高。针对以上问题，本文首先建立一种对非关键帧进行测量率分配的模型。然后根据所建立的分配模型，利用帧间相关性提出一种快速的自适应测量率分配算法。在该算法中，以当前帧对帧组重构的贡献率为参考标准来决定该帧的测量率。实验结果表明，在相同压缩比的条件下，将本文所提出的自适应测量率分配算法应用到分布式视频压缩感知中能有效提升视频重构质量。 

Distributed Compressive Video Sensing by Adaptive Allocation of Frame Measuring Rate

The traditional distributed compressive video sensing usually samples each non-key frame with the same measuring rate. This method usually leads to an inferior reconstruction quality because it does not take into account the correlation-difference among different frames. To address this problem, we formulate a new allocation model for the measuring rate of each non-key frame firstly .Then, an effectively adaptive allocation algorithm which utilizes the inter-frame correlation is proposed according to the new allocation model. The proposed algorithm allocates the measuring rate of each non-key frame by exploiting its contribution to the reconstruction of current GOP (Group of Pictures). The experiment results show that, in the same sampling rate, the reconstructed video quality is improved when taking the proposed adaptive allocation approach into the scheme.