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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22548

Title: Quantitative Comparison of Lossless Video Compression for Multi-Camera Stereo and View Interpolation Applications
Authors: Bao, Yu
Stukken, Bart
Stals, Jef
Chen, Caikou
Claesen, Luc
Issue Date: 2015
Publisher: IEEE
Citation: 13th IEEE International NEW Circuits and Systems Conference, Grenoble, France, 07-10/june/2015
Series/Report: IEEE International New Circuits and Systems Conference
Abstract: Computational video multi-camera systems allow novel applications such as stereo-vision and view interpolation. The computational-as well as communication and storage requirements for real-time multi-camera video are huge. High quality stereo-and view interpolation applications require the accurate combination of detailed image features in two or more cameras. The use of lossy video compression algorithms often lowers the accuracy of small details and textures that are probably not noticeable by a human viewer, but that are crucial in disparity calculations, matching, video stitching and 3D model synthesis. This paper makes a quantitative comparison of two lossless video compression methods. The intention is to use them for efficient implementation in System-on-Chip (SoC) architectures in computational camera systems. The methods compared are based on predictive-corrective compression and Huffman encoding as well as derived methods. For efficient hardware implementation alternative methods for the use of the Huffman coding are investigated. The comparison includes the use of Huffman encoding parameters from previous frames in the compression of current frames.
Notes: [Bao, Yu; Stals, Jef; Chen, Caikou] Yangzhou Univ, Commun Engn, Yangzhou 225009, Jiangsu, Peoples R China. [Bao, Yu; Stukken, Bart; Stals, Jef; Claesen, Luc] Hasselt Univ, Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/22548
DOI: 10.1109/NEWCAS.2015.7182116
ISI #: 000380523300139
ISBN: 9781479988938
ISSN: 2472-467X
Category: C2
Type: Proceedings Paper
Validation: ecoom, 2017
Appears in Collections: Research publications

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