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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Veröffentlicht in:Multimedia tools and applications Jg. 72; H. 1; S. 95 - 121
Hauptverfasser: Trocan, Maria, Tramel, Eric W., Fowler, James E., Pesquet, Beatrice
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.09.2014
Springer
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-012-1330-7