论文标题
实时音调映射:最先进的报告
Real-time Tone Mapping: A State of the Art Report
论文作者
论文摘要
对高质量显示的需求不断上升,随之而来的是高动态范围(HDR)成像的积极研究,这有可能取代标准的动态范围成像。这是由于HDR的功能,例如场景的准确可重复性,其整个可见光和颜色深度。但是此功能带有昂贵的捕获,显示,存储和分销资源要求。另外,在具有有限动态范围的普通显示器上显示HDR图像/视频内容需要某种形式的适应。在过去的几十年中,已经研究并提出了许多适应算法,称为音调映射操作员。在此最新报告中,我们对50多种音调映射算法进行了全面调查,这些算法已在硬件上实施,以加速和实时性能。这些算法已进行了调整或重新设计,以使它们适合硬件。所有实时应用程序均构成严格的定时约束,这需要算法的时间精确处理。这项设计挑战需要新颖的解决方案,在本报告中,我们将重点放在这些问题上。在此中,我们的调查将讨论在GPU [1-10],FPGA [11-41]和ASIC [42-53]上实施的ToneMAP算法,以其硬件规格和性能。输出图像质量是TONEMAP算法的重要指标。从我们的文献调查中,我们发现,已使用各种客观质量指标来证明在硬件平台上调整算法的功能。我们已经编译并研究了本调查中使用的所有指标[54-67]。最后,在本报告中,我们演示了硬件成本与图像质量之间的联系,从而说明了基本的权衡,这对研究社区很有用。
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 operators, have been studied and proposed in the last few decades. In this state of the art report, we present a comprehensive survey of 50+ tone mapping algorithms that have been implemented on hardware for acceleration and real-time performance. These algorithms have been adapted or redesigned to make them hardware-friendly. All real-time application poses strict timing constraints which requires time exact processing of the algorithm. This design challenge require novel solution, and in this report we focus on these issues. In this we survey will discuss those tonemap algorithms which have been implemented on GPU [1-10], FPGA [11-41], and ASIC [42-53] in terms of their hardware specifications and performance. Output image quality is an important metric for tonemap algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the functionality of adapting the algorithm on hardware platform. We have compiled and studied all the metrics used in this survey [54-67]. Finally, in this report we demonstrate the link between hardware cost and image quality thereby illustrating the underlying trade-off which will be useful for the research community.