Sparse PDF maps for non-linear multi-resolution image operations

Handle URI:
http://hdl.handle.net/10754/575790
Title:
Sparse PDF maps for non-linear multi-resolution image operations
Authors:
Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Sicat, Ronell Barrera ( 0000-0001-7037-1614 ) ; Beyer, Johanna; Krüger, Jens J.; Möller, Torsten
Abstract:
We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters. © 2012 ACM.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Visual Computing Center (VCC)
Publisher:
Association for Computing Machinery (ACM)
Journal:
ACM Transactions on Graphics
Conference/Event name:
Proceedings of ACM SIGGRAPH Asia 2012
Issue Date:
1-Nov-2012
DOI:
10.1145/2366145.2366152
Type:
Conference Paper
ISSN:
07300301
Appears in Collections:
Conference Papers; Computer Science Program; Computer Science Program; Visual Computing Center (VCC); Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHadwiger, Markusen
dc.contributor.authorSicat, Ronell Barreraen
dc.contributor.authorBeyer, Johannaen
dc.contributor.authorKrüger, Jens J.en
dc.contributor.authorMöller, Torstenen
dc.date.accessioned2015-08-24T09:26:16Zen
dc.date.available2015-08-24T09:26:16Zen
dc.date.issued2012-11-01en
dc.identifier.issn07300301en
dc.identifier.doi10.1145/2366145.2366152en
dc.identifier.urihttp://hdl.handle.net/10754/575790en
dc.description.abstractWe introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters. © 2012 ACM.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectAnti-aliasingen
dc.subjectBilateral filteringen
dc.subjectDisplay-aware filteringen
dc.subjectImage pyramidsen
dc.subjectLocal Laplacian filteringen
dc.subjectMipmappingen
dc.subjectMultiresolution filteringen
dc.subjectSmoothed local histogram filteringen
dc.titleSparse PDF maps for non-linear multi-resolution image operationsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journalACM Transactions on Graphicsen
dc.conference.date2 – 5 November 2015en
dc.conference.nameProceedings of ACM SIGGRAPH Asia 2012en
dc.conference.locationKobe, Japanen
dc.contributor.institutionIVDA, DFKI, Intel VCI, Germanyen
dc.contributor.institutionSimon Fraser University, Canadaen
kaust.authorHadwiger, Markusen
kaust.authorBeyer, Johannaen
kaust.authorSicat, Ronell Barreraen
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