ICASSP 2016accepted0 citations
Joint-view Kalman-filter recovery of compressed-sensed multiview videos
Ying Liu, Shubham Chamadia, Dimitris A. Pados
Abstract
We develop a novel joint-view Kalman filter for causal reconstruction of compressed-sensed multiview videos. Compressed-sensed multiview video frames are initially reconstructed individually via ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm minimization. Then, ajoint-view state transition model is established for each pair of neighboring views using motion or motion-disparity field estimates. Experimental results demonstrate significantly improved reconstruction quality compared to conventional CS reconstruction and independent-view (single-view) motion-compensated Kalman filtering.
BibTeX
@inproceedings{icassp2016_jointviewkalmanf,
title = {Joint-view Kalman-filter recovery of compressed-sensed multiview videos},
author = {Ying Liu and Shubham Chamadia and Dimitris A. Pados},
booktitle = {ICASSP 2016},
year = {2016}
}