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Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

Oskarsson, Magnus LU (2016) In Journal of Mathematical Imaging and Vision
Abstract

The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in (Formula presented.) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-the-art tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our... (More)

The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in (Formula presented.) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-the-art tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our algorithm on a number of standard high dynamic range images and video sequences and give qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms.

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author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Clustering, Dynamic programming, High dynamic range images, High dynamic range video
in
Journal of Mathematical Imaging and Vision
pages
14 pages
publisher
Springer
external identifiers
  • Scopus:84979220588
ISSN
0924-9907
DOI
10.1007/s10851-016-0677-1
language
English
LU publication?
yes
id
41b11310-7106-4f92-bdc0-a767cd5365b2
date added to LUP
2016-09-05 09:51:43
date last changed
2017-01-01 08:33:17
@article{41b11310-7106-4f92-bdc0-a767cd5365b2,
  abstract     = {<p>The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in (Formula presented.) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-the-art tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our algorithm on a number of standard high dynamic range images and video sequences and give qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms.</p>},
  author       = {Oskarsson, Magnus},
  issn         = {0924-9907},
  keyword      = {Clustering,Dynamic programming,High dynamic range images,High dynamic range video},
  language     = {eng},
  month        = {07},
  pages        = {14},
  publisher    = {Springer},
  series       = {Journal of Mathematical Imaging and Vision},
  title        = {Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering},
  url          = {http://dx.doi.org/10.1007/s10851-016-0677-1},
  year         = {2016},
}