Abstract:

To solve the problem that the accuracy of existing multi-hypothesis prediction methods for compressed video sensing is not high, a multi-hypothesis prediction method based on structural feature priori constrained two-phase reconstruction is proposed. Starting from the non-local similarity and the gradient sparsity of the similar image blocks, the current frame reconstructed by the multi-hypothesis prediction is directly used as the initial frame of the second phase reconstruction. The low rank regularization and the total variation regularization are utilized to perform the second phase reconstruction, wherein the low rank regularization matrix includes the similar blocks of intra-frames and inter-frames, fully utilizing the structural similarity between intra-frames, and improving reconstruction performance effectively, laying the foundation for the subsequent reconstruction of residuals. Simulation experiments show that the proposed two-stage reconstruction algorithm preserves the details of video frames better and has a higher reconstruction accuracy than several excellent reconstruction algorithms.

Key words: video compressed sensing, multi-hypothesis prediction, structure prior, low rank, total variation