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Multiview image compression using a layer-based representation

dc.contributor.authorGelman, Andriyen_GB
dc.contributor.authorDragotti, Pier Luigien_GB
dc.contributor.authorVelisavljević, Vladanen_GB
dc.date.accessioned2013-05-22T11:15:14Z
dc.date.available2013-05-22T11:15:14Z
dc.date.issued2010
dc.identifier.citationGelman, A., Dragotti, P. L. and Velisavljevic, V. (2010) 'Multiview image compression using a layer-based representation', 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, 26-29 September. Hong Kong: IEEE, pp.493-496en_GB
dc.identifier.isbn978-1-4244-7993-1
dc.identifier.doi10.1109/ICIP.2010.5651160
dc.identifier.urihttp://hdl.handle.net/10547/292611
dc.description.abstractThe authors propose a novel compression method for multiview images. The algorithm exploits the layer-based representation, which partitions the data set into planar layers characterized by a constant depth value. For efficient compression, the partitioned data is decorrelated using the separable three-dimensional wavelet transform across the viewpoint and spatial dimensions. The transform is modified to efficiently deal with occlusions and disparity variations for different depths. The generated transform coefficients are entropy coded. Experimental results show that our coding method is capable of outperforming the state-of-the-art algorithms, like H.264/AVC, for different data sets.
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5651160en_GB
dc.subjectdata compressionen_GB
dc.subjectimage codingen_GB
dc.subjectimage representationen_GB
dc.subjectwavelet transformsen_GB
dc.titleMultiview image compression using a layer-based representationen
dc.typeConference papers, meetings and proceedingsen
html.description.abstractThe authors propose a novel compression method for multiview images. The algorithm exploits the layer-based representation, which partitions the data set into planar layers characterized by a constant depth value. For efficient compression, the partitioned data is decorrelated using the separable three-dimensional wavelet transform across the viewpoint and spatial dimensions. The transform is modified to efficiently deal with occlusions and disparity variations for different depths. The generated transform coefficients are entropy coded. Experimental results show that our coding method is capable of outperforming the state-of-the-art algorithms, like H.264/AVC, for different data sets.


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