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Application-specific tone mapping via genetic programming

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Debattista, Kurt (2018) Application-specific tone mapping via genetic programming. Computer Graphics Forum, 37 (1). pp. 439-450. doi:10.1111/cgf.13307 ISSN 0167-7055.

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Official URL: http://dx.doi.org/10.1111/cgf.13307

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Abstract

High dynamic range (HDR) imagery permits the manipulation of real-world data distinct from the limitations of the traditional, low dynamic range (LDR), content. The process of retargetting HDR content to traditional LDR imagery via tone mapping operators (TMOs) is useful for visualising HDR content on traditional displays, supporting
backwards-compatible HDR compression and, more recently, is being frequently used for input into a wide variety of computer vision applications. This work presents the automatic generation of TMOs for specific applications via the evolutionary computing method of genetic programming (GP). A straightforward, generic GP method that generates TMOs for a given fitness function and HDR content is presented. Its efficacy is demonstrated in the context of three applications: visualisation of HDR content on LDR displays, feature mapping and compression. For these applications, results show good performance for the generated TMOs when compared to traditional methods. Furthermore, they demonstrate that the method is generalisable and could be used across various applications that require TMOs but for which dedicated successful TMOs have not yet been discovered.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Image processing, Computer vision , Pattern recognition systems, Genetic programming (Computer science)
Journal or Publication Title: Computer Graphics Forum
Publisher: Blackwell Publishing
ISSN: 0167-7055
Official Date: February 2018
Dates:
DateEvent
February 2018Published
4 November 2017Available
29 September 2017Accepted
Volume: 37
Number: 1
Page Range: pp. 439-450
DOI: 10.1111/cgf.13307
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 6 November 2017
Date of first compliant Open Access: 1 November 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
IF130053Royal Societyhttp://dx.doi.org/10.13039/501100000288

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