2.50
Hdl Handle:
http://hdl.handle.net/10547/292640
Title:
Image representation and compression using directionlets
Authors:
Velisavljević, Vladan ( 0000-0001-9980-9368 ) ; Beferull-Lozano, Baltasar; Vetterli, Martin; Luigi Dragotti, Pier; Van De Ville, Dimitri; Goyal, Vivek K.; Papadakis, Manos
Abstract:
The standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to capture efficiently one-dimensional (1-D) discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. We propose a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. We also demonstrate the outperforming non-linear approximation (NLA) results achieved by our transforms and we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method beats the standard SFQ both in terms of mean-square-error (MSE) and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm.
Citation:
Velisavljevic, V., Beferull-Lozano, B., Vetterli, M. and Dragotti, P.L. (2007) 'Image representation and compression using directionlets', SPIE Photonics & Optics, Wavelets XII, San Diego, California, USA, 26-30 August. San Diego: SPIE, vol.6701.
Publisher:
SPIE
Issue Date:
2007
URI:
http://hdl.handle.net/10547/292640
DOI:
10.1117/12.730454
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1322852
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9780819468499
Appears in Collections:
Centre for Wireless Research (CWR)

Full metadata record

DC FieldValue Language
dc.contributor.authorVelisavljević, Vladanen_GB
dc.contributor.authorBeferull-Lozano, Baltasaren_GB
dc.contributor.authorVetterli, Martinen_GB
dc.contributor.authorLuigi Dragotti, Pieren_GB
dc.contributor.authorVan De Ville, Dimitrien_GB
dc.contributor.authorGoyal, Vivek K.en_GB
dc.contributor.authorPapadakis, Manosen_GB
dc.date.accessioned2013-05-22T15:55:34Z-
dc.date.available2013-05-22T15:55:34Z-
dc.date.issued2007-
dc.identifier.citationVelisavljevic, V., Beferull-Lozano, B., Vetterli, M. and Dragotti, P.L. (2007) 'Image representation and compression using directionlets', SPIE Photonics & Optics, Wavelets XII, San Diego, California, USA, 26-30 August. San Diego: SPIE, vol.6701.en_GB
dc.identifier.isbn9780819468499-
dc.identifier.doi10.1117/12.730454-
dc.identifier.urihttp://hdl.handle.net/10547/292640-
dc.description.abstractThe standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to capture efficiently one-dimensional (1-D) discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. We propose a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. We also demonstrate the outperforming non-linear approximation (NLA) results achieved by our transforms and we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method beats the standard SFQ both in terms of mean-square-error (MSE) and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm.en_GB
dc.language.isoenen
dc.publisherSPIEen_GB
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1322852en_GB
dc.subjectimage processingen_GB
dc.subjectquantizationen_GB
dc.subjecttransform theoryen_GB
dc.subjectwavelet transformsen_GB
dc.titleImage representation and compression using directionletsen
dc.typeConference papers, meetings and proceedingsen
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