liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real-time noise-aware tone mapping
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Computer Graphics and Image Processing)
University of Cambridge.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7765-1747
2015 (English)In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, ISSN 0730-0301, Vol. 34, no 6, p. 198:1-198:15, article id 198Article in journal (Refereed) Published
Abstract [en]

Real-time high quality video tone mapping is needed for manyapplications, such as digital viewfinders in cameras, displayalgorithms which adapt to ambient light, in-camera processing,rendering engines for video games and video post-processing. We propose a viable solution for these applications by designing a videotone-mapping operator that controls the visibility of the noise,adapts to display and viewing environment, minimizes contrastdistortions, preserves or enhances image details, and can be run inreal-time on an incoming sequence without any preprocessing. To ourknowledge, no existing solution offers all these features. Our novelcontributions are: a fast procedure for computing local display-adaptivetone-curves which minimize contrast distortions, a fast method for detailenhancement free from ringing artifacts, and an integrated videotone-mapping solution combining all the above features.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2015. Vol. 34, no 6, p. 198:1-198:15, article id 198
Keywords [en]
Tone mapping, high dynamic range video, display algorithms
National Category
Computer Sciences Media Engineering
Identifiers
URN: urn:nbn:se:liu:diva-122681DOI: 10.1145/2816795.2818092ISI: 000363671200035OAI: oai:DiVA.org:liu-122681DiVA, id: diva2:871464
Conference
SIGGRAPH Aisa 2015
Projects
VPS
Funder
Swedish Foundation for Strategic Research Available from: 2015-11-14 Created: 2015-11-14 Last updated: 2018-05-15
In thesis
1. The high dynamic range imaging pipeline: Tone-mapping, distribution, and single-exposure reconstruction
Open this publication in new window or tab >>The high dynamic range imaging pipeline: Tone-mapping, distribution, and single-exposure reconstruction
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Techniques for high dynamic range (HDR) imaging make it possible to capture and store an increased range of luminances and colors as compared to what can be achieved with a conventional camera. This high amount of image information can be used in a wide range of applications, such as HDR displays, image-based lighting, tone-mapping, computer vision, and post-processing operations. HDR imaging has been an important concept in research and development for many years. Within the last couple of years it has also reached the consumer market, e.g. with TV displays that are capable of reproducing an increased dynamic range and peak luminance.

This thesis presents a set of technical contributions within the field of HDR imaging. First, the area of HDR video tone-mapping is thoroughly reviewed, evaluated and developed upon. A subjective comparison experiment of existing methods is performed, followed by the development of novel techniques that overcome many of the problems evidenced by the evaluation. Second, a largescale objective comparison is presented, which evaluates existing techniques that are involved in HDR video distribution. From the results, a first open-source HDR video codec solution, Luma HDRv, is built using the best performing techniques. Third, a machine learning method is proposed for the purpose of reconstructing an HDR image from one single-exposure low dynamic range (LDR) image. The method is trained on a large set of HDR images, using recent advances in deep learning, and the results increase the quality and performance significantly as compared to existing algorithms.

The areas for which contributions are presented can be closely inter-linked in the HDR imaging pipeline. Here, the thesis work helps in promoting efficient and high-quality HDR video distribution and display, as well as robust HDR image reconstruction from a single conventional LDR image.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 132
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1939
Keywords
high dynamic range imaging, tone-mapping, video tone-mapping, HDR video encoding, HDR image reconstruction, inverse tone-mapping, machine learning, deep learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-147843 (URN)10.3384/diss.diva-147843 (DOI)9789176853023 (ISBN)
Public defence
2018-06-08, Domteatern, Visualiseringscenter C, Kungsgatan 54, Campus Norrköping, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textProject web page

Authority records BETA

Eilertsen, GabrielUnger, Jonas

Search in DiVA

By author/editor
Eilertsen, GabrielUnger, Jonas
By organisation
Media and Information TechnologyFaculty of Science & Engineering
In the same journal
ACM Transactions on Graphics
Computer SciencesMedia Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 386 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf