Video Compressed Sensing with Multihypothesis
E. W. Tramel & J. E. Fowler
Astract
The compressed-sensing recovery of video sequences driven by multihypothesis predictions is considered. Specifically, multihypothesis predictions of the current frame are used to generate a residual in the domain of the compressed-sensing random projections. This residual being typically more compressible than the original frame leads to improved reconstruction quality. To appropriately weight the hypothesis predictions, a Tikhonov regularization to an ill-posed least-squares optimization is proposed. This method is shown to outperform both recovery of the frame independently of the others as well as recovery based on single-hypothesis prediction.
BibTeX
inproceedings{tf2011,
Address = {Snowbird, Utah},
Author = {Eric W. Tramel and James E. Fowler},
Booktitle = {Proc. of the {IEEE} Data Compression Conf. (DCC)},
Month = march,
Pages = {193--202},
Title = {Video Compressed Sensing with Multihypothesis},
Year = 2011}