2.50
Hdl Handle:
http://hdl.handle.net/10547/292583
Title:
Space-frequency quantization for image compression with directionlets
Authors:
Velisavljević, Vladan ( 0000-0001-9980-9368 ) ; Beferull-Lozano, Baltasar; Vetterli, Martin
Abstract:
The standard separable 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 efficiently capture 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 the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error 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. and Vetterli, M. (2007) 'Space-frequency quantization for image compression with directionlets', IEEE Transactions on Image Processing, 16 (7), pp.1761-1773.
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Journal:
IEEE Transactions on Image Processing
Issue Date:
2007
URI:
http://hdl.handle.net/10547/292583
DOI:
10.1109/TIP.2007.899183
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4237209
Type:
Article
Language:
en
ISSN:
1057-7149
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.date.accessioned2013-05-21T14:27:34Z-
dc.date.available2013-05-21T14:27:34Z-
dc.date.issued2007-
dc.identifier.citationVelisavljevic, V., Beferull-Lozano, B. and Vetterli, M. (2007) 'Space-frequency quantization for image compression with directionlets', IEEE Transactions on Image Processing, 16 (7), pp.1761-1773.en_GB
dc.identifier.issn1057-7149-
dc.identifier.doi10.1109/TIP.2007.899183-
dc.identifier.urihttp://hdl.handle.net/10547/292583-
dc.description.abstractThe standard separable 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 efficiently capture 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 the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error 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.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4237209en_GB
dc.subjectdirectional transformsen_GB
dc.subjectimage codingen_GB
dc.subjectimage reconstructionen_GB
dc.subjectspace-frequency quantizationen_GB
dc.subjectsparse representationen_GB
dc.titleSpace-frequency quantization for image compression with directionletsen
dc.typeArticleen
dc.identifier.journalIEEE Transactions on Image Processingen_GB
All Items in UOBREP are protected by copyright, with all rights reserved, unless otherwise indicated.