Multi-Hypothesis Compressed Video Sensing Technique

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2014-12
Main Authors: Azghani, Masoumeh, Karimi, Mostafa, Marvasti, Farokh
Format: Article
Language:English
Subjects:
Computer simulation
Decoding
Hypotheses
Iterative algorithms
Iterative methods
Regularization
Sequences
Video compression
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422