Application-Specific Tone Mapping Via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Debattista:2018:cgf,

• author = "Kurt Debattista",
• title = "Application-Specific Tone Mapping Via Genetic Programming",
• journal = "Computer Graphics Forum",
• year = "2018",
• volume = "37",
• number = "1",
• pages = "439--450",
• month = "1 " # nov,
• keywords = "genetic algorithms, genetic programming, high dynamic range imaging, tone mapping",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cgf/cgf37.html#Debattista18",
• DOI = "doi:10.1111/cgf.13307",
• size = "12 pages",
• 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 retargeting HDR content to traditional LDR imagery via tone mapping operators (TMOs) is useful for visualizing 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: Visualization 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 generalizable and could be used across various applications that require TMOs but for which dedicated successful TMOs have not yet been discovered.",
• notes = "journals/cgf/Debattista18",

}

Genetic Programming entries for Kurt Debattista

Citations