Multi-Hypothesis Compressed Video Sensing Technique
This addresses video compression for applications like streaming or storage, but it appears incremental as it builds on existing methods like Elasticnet and Tikhonov.
The paper tackles video compression by proposing a compressive sampling and multi-hypothesis reconstruction strategy with a simple encoder, resulting in a new iterative algorithm that outperforms Elasticnet and Tikhonov in recovery performance and is computationally faster than Elasticnet.
In this paper, we present a compressive sampling and Multi-Hypothesis (MH) reconstruction strategy for video sequences which has a rather simple encoder, while the decoding system is not that complex. We introduce a convex cost function that incorporates the MH technique with the sparsity constraint and the Tikhonov regularization. Consequently, we derive a new iterative algorithm based on these criteria. This algorithm surpasses its counterparts (Elasticnet and Tikhonov) in the recovery performance. Besides it is computationally much faster than the Elasticnet and comparable to the Tikhonov. Our extensive simulation results confirm these claims.