How pseudo model may help evaluate image compression on spiral architecture

Wang, H; Hintz, T; He, X; Wu, Q

How pseudo model may help evaluate image compression on spiral architecture

Wang, H Hintz, T He, X

Wu, Q

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA'04, 2004, 1 pp. 389 - 394
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Hintz, T	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA'04, 2004, 1 pp. 389 - 394	en_US
dc.identifier.isbn	1932415262	en_US
dc.identifier.isbn	9781932415261	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12643
dc.description.abstract	Although Spiral Architecture (SA) has many advantages in image processing and machine vision, there is no available image capture device yet to support this structure. Hence, in order to implement our theoretical results, it is necessary to construct the SA from the existing image structure, on which the traditional image representation is based. Therefore, in this paper we provide two methods, Mimic model and Pseudo model. We can construct the architecture from the rectangular pixels and thus they share the Spiral addressing mechanism. This paper then presents how Pseudo model may help to evaluate the performance of image compression on Spiral Architecture. Based on the two unique properties of Spiral Architecture, i.e. locality of light intensity and uniformity of image partitioning, possible approaches to image compression may be discovered. Our future work would focus on partitioning the image into blocks and implement compression on the transform domain.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA'04	en_US
dc.title	How pseudo model may help evaluate image compression on spiral architecture	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	La Vegas, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Although Spiral Architecture (SA) has many advantages in image processing and machine vision, there is no available image capture device yet to support this structure. Hence, in order to implement our theoretical results, it is necessary to construct the SA from the existing image structure, on which the traditional image representation is based. Therefore, in this paper we provide two methods, Mimic model and Pseudo model. We can construct the architecture from the rectangular pixels and thus they share the Spiral addressing mechanism. This paper then presents how Pseudo model may help to evaluate the performance of image compression on Spiral Architecture. Based on the two unique properties of Spiral Architecture, i.e. locality of light intensity and uniformity of image partitioning, possible approaches to image compression may be discovered. Our future work would focus on partitioning the image into blocks and implement compression on the transform domain.

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http://hdl.handle.net/10453/12643