Compressed-sensing recovery of multiview image and video sequences using signal prediction

In the compressed sensing of multiview images and video sequences, signal prediction is incorporated into the reconstruction process in order to exploit the high degree of interview and temporal correlation common to multiview scenarios. Instead of recovering each individual frame independently, nei...

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Vydáno v:	Multimedia tools and applications Ročník 72; číslo 1; s. 95 - 121
Hlavní autoři:	Trocan, Maria, Tramel, Eric W., Fowler, James E., Pesquet, Beatrice
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.09.2014 Springer Springer Nature B.V
Témata:	Algorithms Analysis Applied sciences Artificial intelligence Benchmarking Cameras Computer Communication Networks Computer Science Computer science; control theory; systems Data compression Data Structures and Information Theory Detection, estimation, filtering, equalization, prediction Exact sciences and technology Frames Gain Information, signal and communications theory Mathematical analysis Multimedia Multimedia computer applications Multimedia Information Systems Pattern recognition. Digital image processing. Computational geometry Performance evaluation Reconstruction Recovery Sensors Signal and communications theory Signal, noise Special Purpose and Application-Based Systems Studies Surveillance Telecommunications and information theory Temporal logic Signal prediction Multiviews Compressed sensing Multiple view Correlation compressed sensing Image processing Image sequence Video signal Sparse representation Recovery Signal detection
ISSN:	1380-7501, 1573-7721
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Abstract	In the compressed sensing of multiview images and video sequences, signal prediction is incorporated into the reconstruction process in order to exploit the high degree of interview and temporal correlation common to multiview scenarios. Instead of recovering each individual frame independently, neighboring frames in both the view and temporal directions are used to calculate a prediction of a target frame, and the difference is used to drive a residual-based compressed-sensing reconstruction. The proposed approach demonstrates a significant gain in reconstruction quality relative to the straightforward compressed-sensing recovery of each frame independently of the others in the multiview set, as well as a significant performance advantage as compared to a pair of benchmark multiple-frame compressed-sensing reconstructions.
AbstractList	In the compressed sensing of multiview images and video sequences, signal prediction is incorporated into the reconstruction process in order to exploit the high degree of interview and temporal correlation common to multiview scenarios. Instead of recovering each individual frame independently, neighboring frames in both the view and temporal directions are used to calculate a prediction of a target frame, and the difference is used to drive a residual-based compressed-sensing reconstruction. The proposed approach demonstrates a significant gain in reconstruction quality relative to the straightforward compressed-sensing recovery of each frame independently of the others in the multiview set, as well as a significant performance advantage as compared to a pair of benchmark multiple-frame compressed-sensing reconstructions.[PUBLICATION ABSTRACT] In the compressed sensing of multiview images and video sequences, signal prediction is incorporated into the reconstruction process in order to exploit the high degree of interview and temporal correlation common to multiview scenarios. Instead of recovering each individual frame independently, neighboring frames in both the view and temporal directions are used to calculate a prediction of a target frame, and the difference is used to drive a residual-based compressed-sensing reconstruction. The proposed approach demonstrates a significant gain in reconstruction quality relative to the straightforward compressed-sensing recovery of each frame independently of the others in the multiview set, as well as a significant performance advantage as compared to a pair of benchmark multiple-frame compressed-sensing reconstructions.
Author	Trocan, Maria Pesquet, Beatrice Fowler, James E. Tramel, Eric W.
Author_xml	– sequence: 1 givenname: Maria surname: Trocan fullname: Trocan, Maria email: maria.trocan@isep.fr organization: Institut Supérieur d’Électronique de Paris – sequence: 2 givenname: Eric W. surname: Tramel fullname: Tramel, Eric W. organization: Geosystems Research Institute, Mississippi State University – sequence: 3 givenname: James E. surname: Fowler fullname: Fowler, James E. organization: Geosystems Research Institute, Mississippi State University – sequence: 4 givenname: Beatrice surname: Pesquet fullname: Pesquet, Beatrice organization: Télécom ParisTech
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CitedBy_id	crossref_primary_10_1007_s11042_016_3491_2 crossref_primary_10_1088_1361_6501_acf597 crossref_primary_10_1016_j_jvcir_2015_09_020 crossref_primary_10_1049_iet_ipr_2019_0295 crossref_primary_10_3390_app10227963 crossref_primary_10_1016_j_neucom_2017_07_037 crossref_primary_10_1016_j_jnca_2018_05_018 crossref_primary_10_1002_cpe_7479 crossref_primary_10_1109_TMM_2017_2654123 crossref_primary_10_1109_LSP_2016_2527680 crossref_primary_10_1109_TCSVT_2017_2656920 crossref_primary_10_1007_s00530_017_0551_z
Cites_doi	10.1109/TIT.2007.909108 10.1109/MSP.2007.914731 10.1002/mrm.21757 10.1049/el.2010.2325 10.1002/ima.20231 10.1561/2000000033 10.1109/TSP.2010.2048105 10.1109/TIT.2006.885507 10.1109/MSP.2007.914730 10.1186/1687-6180-2012-37 10.1006/acha.2000.0343 10.1002/mrm.21477 10.1109/TIP.2007.909319 10.1137/080724265 10.1109/MSP.2006.1657820 10.1137/S1064827596304010 10.1016/j.acha.2009.04.002 10.1109/TSP.2010.2051150 10.1016/0167-2789(92)90242-F 10.1109/MSP.2007.904808 10.1007/s002110050258 10.1109/ICASSP.2009.4959603 10.1109/ICDSP.2007.4288604 10.1109/DCC.2010.90 10.1109/ICIP.2010.5652767 10.1109/ICIP.2006.312577 10.1109/MMSP.2010.5662003 10.1109/ICIP.2009.5414208 10.1109/PCS.2009.5167356 10.1109/ICIP.2008.4711899 10.1109/ICASSP.2009.4959756 10.1109/ICME.2010.5583411 10.4171/022-3/69
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Keywords	Signal prediction Multiviews Compressed sensing Multiple view Correlation compressed sensing Image processing Image sequence Video signal Sparse representation Recovery Signal detection
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SubjectTerms	Algorithms Analysis Applied sciences Artificial intelligence Benchmarking Cameras Computer Communication Networks Computer Science Computer science; control theory; systems Data compression Data Structures and Information Theory Detection, estimation, filtering, equalization, prediction Exact sciences and technology Frames Gain Information, signal and communications theory Mathematical analysis Multimedia Multimedia computer applications Multimedia Information Systems Pattern recognition. Digital image processing. Computational geometry Performance evaluation Reconstruction Recovery Sensors Signal and communications theory Signal, noise Special Purpose and Application-Based Systems Studies Surveillance Telecommunications and information theory Temporal logic
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Title	Compressed-sensing recovery of multiview image and video sequences using signal prediction
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