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Real-Time Tone Mapping : A Survey and Cross-Implementation Hardware Benchmark

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Title: Real-Time Tone Mapping : A Survey and Cross-Implementation Hardware Benchmark
Authors: Ou, Yafei Browse this author
Ambalathankandy, Prasoon Browse this author
Takamaeda, Shinya Browse this author →KAKEN DB
Motomura, Masato Browse this author
Asai, Tetsuya Browse this author →KAKEN DB
Ikebe, Masayuki Browse this author →KAKEN DB
Keywords: Graphics processing units
Field programmable gate arrays
Dynamic range
Image sensors
Real-time systems
Tone mapping
computational complexity
high dynamic range
image sensor
Issue Date: May-2022
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE transactions on circuits and systems for video technology
Volume: 32
Issue: 5
Start Page: 2666
End Page: 2686
Publisher DOI: 10.1109/TCSVT.2021.3060143
Abstract: The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR's features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Also, display of HDR images/video content on an ordinary display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping (TM) operators, have been studied and proposed in the last few decades. In this article, we present a comprehensive survey of 60 TM algorithms that have been implemented on hardware for acceleration and real-time performance. In this state-of-the-art survey, we will discuss those TM algorithms which have been implemented on GPU, FPGA, and ASIC in terms of their hardware specifications and performance. Output image quality is an important metric for TM algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the quality of those algorithms hardware implementation. We have compiled those metrics used in this survey, and analyzed the relationship between hardware cost, image quality and computational efficiency. Currently, machine learning-based (ML) algorithms have become an important tool to solve many image processing tasks, and this article concludes with a discussion on the future research directions to realize ML-based TM operators on hardware.
Type: article
Appears in Collections:量子集積エレクトロニクス研究センター (Research Center for Integrated Quantum Electronics) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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