Real-time tone mapping: a survey and cross-implementation hardware benchmark
Real-time tone mapping: a survey and cross-implementation hardware benchmark
复制标题
实时色调映射：调查和交叉实现硬件基准
DOI：
10.1109/tcsvt.2021.3060143
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发表时间：
2021
影响因子：
8.4
通讯作者：
Masayuki Ikebe
中科院分区：
文献类型：
--
作者：
Yafei Ou;Prasoon Ambalathankandy;Shinya Takamaeda-Yamazaki;Masato Motomura;Tetsuya Asai;Masayuki Ikebe
高动态范围(HDR)成像技术具有取代标准动态范围成像的潜力，随着高质量显示需求的增加，高动态范围(HDR)成像的研究日益活跃。这是由于HDR的功能，如准确地重现场景及其整个可见光和颜色深度的光谱。但这种能力伴随着昂贵的捕获、显示、存储和分发资源需求。此外，在具有有限动态范围的普通显示设备上显示HDR图像/视频内容需要某种形式的适配。在过去的几十年中，已经研究并提出了许多自适应算法，它们被广泛地称为色调映射(TM)算子。在这篇文章中，我们提供了一个全面的调查，60个TM算法已经在硬件上实现的加速和实时性能。在这篇最新的综述中，我们将讨论已经在GPU、FPGA和ASIC上实现的TM算法的硬件规格和性能。输出图像质量是TM算法的一项重要指标。从我们的文献调查中我们发现，各种客观的质量度量已经被用来证明这些算法硬件实现的质量。我们整理了本次调查中使用的指标，并分析了硬件成本、图像质量和计算效率之间的关系。目前，基于机器学习(ML)的算法已经成为解决许多图像处理任务的重要工具，本文最后讨论了基于ML的TM算子在硬件上实现的未来研究方向。
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.