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The high dynamic range imaging pipeline: Tone-mapping, distribution, and single-exposure reconstruction
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
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 [en]
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: urn:nbn:se:liu:diva-147843DOI: 10.3384/diss.diva-147843ISBN: 9789176853023 (print)OAI: oai:DiVA.org:liu-147843DiVA, id: diva2:1206025
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
List of papers
1. A comparative review of tone-mapping algorithms for high dynamic range video
Open this publication in new window or tab >>A comparative review of tone-mapping algorithms for high dynamic range video
2017 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 2, p. 565-592Article in journal (Refereed) Published
Abstract [en]

Tone-mapping constitutes a key component within the field of high dynamic range (HDR) imaging. Its importance is manifested in the vast amount of tone-mapping methods that can be found in the literature, which are the result of an active development in the area for more than two decades. Although these can accommodate most requirements for display of HDR images, new challenges arose with the advent of HDR video, calling for additional considerations in the design of tone-mapping operators (TMOs). Today, a range of TMOs exist that do support video material. We are now reaching a point where most camera captured HDR videos can be prepared in high quality without visible artifacts, for the constraints of a standard display device. In this report, we set out to summarize and categorize the research in tone-mapping as of today, distilling the most important trends and characteristics of the tone reproduction pipeline. While this gives a wide overview over the area, we then specifically focus on tone-mapping of HDR video and the problems this medium entails. First, we formulate the major challenges a video TMO needs to address. Then, we provide a description and categorization of each of the existing video TMOs. Finally, by constructing a set of quantitative measures, we evaluate the performance of a number of the operators, in order to give a hint on which can be expected to render the least amount of artifacts. This serves as a comprehensive reference, categorization and comparative assessment of the state-of-the-art in tone-mapping for HDR video.

Place, publisher, year, edition, pages
WILEY, 2017
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-139637 (URN)10.1111/cgf.13148 (DOI)000404474000048 ()
Conference
38th Annual Conference of the European-Association-for-Computer-Graphics (EUROGRAPHICS)
Note

Funding Agencies|Swedish Foundation for Strategic Research (SSF) [IIS11-0081]; Linkoping University Center for Industrial Information Technology (CENIIT); Swedish Research Council through the Linnaeus Environment CADICS

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2018-05-15
2. Evaluation of Tone Mapping Operators for HDR-Video
Open this publication in new window or tab >>Evaluation of Tone Mapping Operators for HDR-Video
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
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-104135 (URN)10.1111/cgf.12235 (DOI)000327310800029 ()
Projects
VPS
Available from: 2014-02-07 Created: 2014-02-07 Last updated: 2018-05-15Bibliographically approved
3. Real-time noise-aware tone mapping
Open this publication in new window or tab >>Real-time noise-aware tone mapping
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
Keywords
Tone mapping, high dynamic range video, display algorithms
National Category
Computer Sciences Media Engineering
Identifiers
urn:nbn:se:liu:diva-122681 (URN)10.1145/2816795.2818092 (DOI)000363671200035 ()
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
4. A HIGH DYNAMIC RANGE VIDEO CODEC OPTIMIZED BY LARGE-SCALE TESTING
Open this publication in new window or tab >>A HIGH DYNAMIC RANGE VIDEO CODEC OPTIMIZED BY LARGE-SCALE TESTING
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), IEEE , 2016, p. 1379-1383Conference paper, Published paper (Refereed)
Abstract [en]

While a number of existing high-bit depth video compression methods can potentially encode high dynamic range (HDR) video, few of them provide this capability. In this paper, we investigate techniques for adapting HDR video for this purpose. In a large-scale test on 33 HDR video sequences, we compare 2 video codecs, 4 luminance encoding techniques (transfer functions) and 3 color encoding methods, measuring quality in terms of two objective metrics, PU-MSSIM and HDR-VDP-2. From the results we design an open source HDR video encoder, optimized for the best compression performance given the techniques examined.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Conference on Image Processing ICIP, ISSN 1522-4880
Keywords
High dynamic range (HDR) video; HDR video coding; perceptual image metrics
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-134106 (URN)10.1109/ICIP.2016.7532584 (DOI)000390782001093 ()978-1-4673-9961-6 (ISBN)
Conference
23rd IEEE International Conference on Image Processing (ICIP)
Available from: 2017-01-22 Created: 2017-01-22 Last updated: 2018-05-15
5. HDR image reconstruction from a single exposure using deep CNNs
Open this publication in new window or tab >>HDR image reconstruction from a single exposure using deep CNNs
Show others...
2017 (English)In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 36, no 6, article id 178Article in journal (Refereed) Published
Abstract [en]

Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2017
Keywords
HDR reconstruction; inverse tone-mapping; deep learning; convolutional network
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-143943 (URN)10.1145/3130800.3130816 (DOI)000417448700008 ()
Conference
10th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia
Note

Funding Agencies|Linkoping University Center for Industrial Information Technology (CENIIT); Swedish Science Council [2015-05180]; Wallenberg Autonomous Systems Program (WASP)

Available from: 2017-12-29 Created: 2017-12-29 Last updated: 2018-05-15

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Output format
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