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

Markus Hadwiger; Ronell Barrera Sicat; Johanna Beyer; Jens J. Krüger; Torsten Möller

doi:10.1145/2366145.2366152

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

Markus Hadwiger, Ronell Barrera Sicat, Johanna Beyer, Jens J. Krüger, Torsten Möller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

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.

Original language	English (US)
Title of host publication	ACM Transactions on Graphics
Publisher	Association for Computing Machinery (ACM)
Pages	1
DOIs	https://doi.org/10.1145/2366145.2366152
State	Published - Nov 1 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design

Access to Document

10.1145/2366145.2366152

Cite this

@inproceedings{58971349c52a460580f70720b7cbe83a,

title = "Sparse PDF maps for non-linear multi-resolution image operations",

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. {\textcopyright} 2012 ACM.",

author = "Markus Hadwiger and Sicat, {Ronell Barrera} and Johanna Beyer and Kr{\"u}ger, {Jens J.} and Torsten M{\"o}ller",

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doi = "10.1145/2366145.2366152",

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T1 - Sparse PDF maps for non-linear multi-resolution image operations

AU - Hadwiger, Markus

AU - Sicat, Ronell Barrera

AU - Beyer, Johanna

AU - Krüger, Jens J.

AU - Möller, Torsten

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2012/11/1

Y1 - 2012/11/1

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/575790

UR - https://dl.acm.org/doi/10.1145/2366145.2366152

UR - http://www.scopus.com/inward/record.url?scp=84870219113&partnerID=8YFLogxK

U2 - 10.1145/2366145.2366152

DO - 10.1145/2366145.2366152

M3 - Conference contribution

SP - 1

BT - ACM Transactions on Graphics

PB - Association for Computing Machinery (ACM)

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

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