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Evaluation of reverse tone mapping through varying exposure conditions

机译:通过不同曝光条件评估反向色调映射

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Most existing image content has low dynamic range (LDR), which necessitates effective methods to display such legacy content on high dynamic range (HDR) devices. Reverse tone mapping operators (rTMOs) aim to take LDR content as input and adjust the contrast intelligently to yield output that recreates the HDR experience. In this paper we show that current rTMO approaches fall short when the input image is not exposed properly. More specifically, we report a series of perceptual experiments using a Brightside HDR display and show that, while existing rTMOs perform well for under-exposed input data, the perceived quality degrades substantially with over-exposure, to the extent that in some cases subjects prefer the LDR originals to images that have been treated with rTMOs. We show that, in these cases, a simple rTMO based on gamma expansion avoids the errors introduced by other methods, and propose a method to automatically set a suitable gamma value for each image, based on the image key and empirical data. We validate the results both by means of perceptual experiments and using a recent image quality metric, and show that this approach enhances visible details without causing artifacts in incorrectly-exposed regions. Additionally, we perform another set of experiments which suggest that spatial artifacts introduced by rTMOs are more disturbing than inaccuracies in the expanded intensities. Together, these findings suggest that when the quality of the input data is unknown, reverse tone mapping should be handled with simple, non-aggressive methods to achieve the desired effect.
机译:大多数现有的图像内容具有低动态范围(LDR),这需要有效的方法在高动态范围(HDR)设备上显示此类传统内容。反向色调映射运算符(RTMOS)旨在将LDR内容视为输入,并智能地调整对比度,以产生重新创建HDR体验的输出。在本文中,我们表明,当输入图像未正确暴露时,当前的RTMO方法掉落。更具体地说,我们报告了一系列使用Brightside HDR显示器的感知实验,并表明现有RTMOS表现出对未暴露的输入数据进行良好,但在某些情况下,在某些情况下,感知的质量大大降低了LDR原稿到已用RTMOS处理的图像。我们表明,在这些情况下,基于伽马膨胀简单rTMO避免了其它方法引入的误差,并提出了一种方法来自动设置为每个图像的合适的伽马值,基于该图像密钥和经验数据。我们通过感知实验和使用最近的图像质量指标来验证结果,并表明该方法增强了可见细节,而不会导致不正确的区域内的伪影。此外,我们执行另一组实验,表明通过RTMO引入的空间伪像比扩展强度的不准确性更令人不安。这些研究结果表明,当输入数据的质量未知时,应用简单的非侵略性方法处理反向音映射以达到所需的效果。

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