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Evaluation of Tone Mapping Operators for HDR-Video
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Bangor University, Wales .
Bangor University, Wales .
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-7765-1747
2013 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 32, no 7, p. 275-284Article in journal (Refereed) Published
Abstract [en]

Eleven tone-mapping operators intended for video processing are analyzed and evaluated with camera-captured and computer-generated high-dynamic-range content. After optimizing the parameters of the operators in a formal experiment, we inspect and rate the artifacts (flickering, ghosting, temporal color consistency) and color rendition problems (brightness, contrast and color saturation) they produce. This allows us to identify major problems and challenges that video tone-mapping needs to address. Then, we compare the tone-mapping results in a pair-wise comparison experiment to identify the operators that, on average, can be expected to perform better than the others and to assess the magnitude of differences between the best performing operators.

Place, publisher, year, edition, pages
Wiley , 2013. Vol. 32, no 7, p. 275-284
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-104135DOI: 10.1111/cgf.12235ISI: 000327310800029OAI: oai:DiVA.org:liu-104135DiVA, id: diva2:694640
Projects
VPS
Available from: 2014-02-07 Created: 2014-02-07 Last updated: 2018-05-15Bibliographically approved
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
Keyword
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

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Eilertsen, GabrielUnger, Jonas

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