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

Ou, Yafei; Ambalathankandy, Prasoon; Shinya, Takamaeda; Motomura, Masato; Asai, Tetsuya; Ikebe, Masayuki

doi:10.1109/tcsvt.2021.3060143

Cited by 12 publications

(7 citation statements)

References 171 publications

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“…Tone mapping Tone mapping is key when dealing with HDR images. This importance is demonstrated by the large amount of techniques that can be found in the literature, in books [15,17,18], as well as surveys [19,20]. TMOs compress the illuminance range while preserving contrast by using a mapping function.…”

Section: Previous Workmentioning

confidence: 99%

Gain compensation across LIDAR scans

Muñoz-Pandiella

Trinidad

Andújar

et al. 2022

Computers & Graphics

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Section: Previous Workmentioning

confidence: 99%

Gain compensation across LIDAR scans

Muñoz-Pandiella

Trinidad

Andújar

et al. 2022

Computers & Graphics

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“…Current imaging devices, which often produce images with a high bit depth, coupled with the maturity of HDR synthesis techniques, lead to the expectation of a growing number of HDR videos in the future. However, most current display devices have a limited range of 8 bits [4], necessitating the use of tone mapping operators (TMOs) to map images into the display range. The TMOs help both highlights and shadows to display more details, providing better subjective quality.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the different purposes of applying TMOs on images, contingent upon the target application [4], diverse TMOs and their hardware implementations have continued to be proposed in recent years. For instance, Refs.…”

Section: Introductionmentioning

confidence: 99%

“…However, this network structure exhibits a runtime of several tens of milliseconds on a GPU [17]. With power and resource constraints in embedded vision applications, a straightforward porting of software algorithms to hardware platforms may result in poor performance or even system failures, failing to meet the increasing demands of numerous embedded vision applications (such as advanced automotive systems, medical imaging, and unmanned aerial vehicles) [4]. The primary components of such vision-based embedded systems include image sensors, image processing algorithms, and display monitors.…”

Section: Introductionmentioning

confidence: 99%

“…The primary components of such vision-based embedded systems include image sensors, image processing algorithms, and display monitors. For these embedded applications with stringent time constraints, hardware acceleration is indispensable, and the ability to build optimized custom hardware makes field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) better than GPUs [4]. The hardware implementation of image processing algorithms must be optimized for hardware portability, as this redesigning effort can leverage hardware platforms to achieve optimal performance.…”

Section: Introductionmentioning

confidence: 99%

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A Low-Latency Noise-Aware Tone Mapping Operator for Hardware Implementation with a Locally Weighted Guided Filter

Liang,

Yan,

Wang

et al. 2024

Symmetry

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A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μs under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods.

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Point Operations

2023

Design for Embedded Image Processing on FPGAs

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

Cited by 12 publications

References 171 publications

Gain compensation across LIDAR scans

Gain compensation across LIDAR scans

A Low-Latency Noise-Aware Tone Mapping Operator for Hardware Implementation with a Locally Weighted Guided Filter

Point Operations

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