Video Compressed Sensing with Multihypothesis

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.

• Type: Journal
• Year: 2011
• Where: DCC
• Authors: Eric W. Tramel, James E. Fowler
• Keywords: Compressed-sensing Random Projections, Computer Graphics, DCC 2011, Ill-posed Least-squares Optimization, Prediction