基于变采样率的多假设预测分块视频压缩感知

Block Compressed Sensing of Video Based on Variable Sampling Rates and Multihypothesis Predictions

现有的分块视频压缩感知通常对所有图像块均采用相同的测量矩阵进行测量,这种平均分配采样率的测量方式忽略了视频中不同区域的结构复杂度和变化程度不同的事实。针对这一问题,该文根据视频帧间相关性的分布特点提出了一种自适应分配采样率的变采样率压缩感知方法。将图像块按照帧间相关性的大小分类并分配不同的采样率,重构过程采用变采样率多假设预测算法以充分利用帧间相关性。实验结果表明该文算法能够在低采样率下重构出高质量的视频图像,而且这种变采样率测量的方式有利于提高运动剧烈区域的重构质量。

For most of those existing block-based compressed sensing of video, the same measurement matrix is usually utilized for all blocks, which underestimates the fact that the structural complexity and the movement varies from different regions. To address this issue, a novel block-based adaptive compressde sensing algorithm with variable sampling rate is proposed according to the distribution characteristics of the correlations between neighboring frames. It classifies blocks into different types depending on their inter-frame correlation, and adjusts the sampling rate accordingly. Multihypothesis predicting algorithm is used to reconstruct the videos to make full use of the inter-frame correlation. The experiment showes that the proposed algorithm reduces the number of sampled measurements while still improving the quality of the reconstructed frames. Also, with the variable sampling rate method, a higher reconstruction quality can be achieved for the regions containing relatively fast movement.