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

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.