Although high dynamic range (HDR) display has become popular recently, the legacy content such as standard dynamic range (SDR) video is still in service and needs to be properly converted on HDR display devices. Therefore, it is desirable for HDR TV sets to have the capability of automatically converting input SDR video into HDR video, which is called reverse tone mapping (RTM). In this paper, we propose a novel learning-based low-complexity RTM scheme that not only expands the suppressed dynamic ranges (DR) of the SDR videos (or images), but also effectively restores lost detail in the SDR videos. Most existing conventional RTM schemes have focused on how to expand the DR of global contrast, resulting in limitations in recovering lost detail of SDR videos. On the other hand, the recent convolutional neural network-based approaches show promising results, but they are too complex to be applied on the users’ devices in practice. In this paper, our learning-based RTM scheme is computationally simple but effective in recovering lost detail. To learn the SDR-to-HDR relation, training “SDR-HDR” images are first separated into their base layer components and detail layer components by applying a guided filter. The detail layer components of the “SDR-HDR” pairs are used to train the SDR-to-HDR mapping. The mapping matrices are computed based on kernel ridge regression. In the meantime, the global contrast of the base layers is expanded by a nonlinear function that suppresses darker regions and amplifies brighter regions to fit the full DR of a target HDR display. To verify the effectiveness of our learning-based RTM scheme, we performed subjective quality assessment for images and videos. The experimental results show that our RTM scheme outperforms the existing RTM scheme with the successful restoration of lost detail in SDR images.

中文翻译：

---

基于学习的低复杂度反向色调映射与线性映射

尽管高动态范围 (HDR) 显示最近变得流行，但标准动态范围 (SDR) 视频等遗留内容仍在使用中，需要在 HDR 显示设备上正确转换。因此，HDR 电视机需要具备将输入的 SDR 视频自动转换为 HDR 视频的能力，这称为逆色调映射 (RTM)。在本文中，我们提出了一种新颖的基于学习的低复杂度 RTM 方案，它不仅扩展了 SDR 视频（或图像）的抑制动态范围 (DR)，而且还有效地恢复了 SDR 视频中丢失的细节。大多数现有的传统RTM方案都专注于如何扩展全局对比度的DR，导致在恢复SDR视频丢失的细节方面存在局限性。另一方面，最近基于卷积神经网络的方法显示出有希望的结果，但它们太复杂而无法在实践中应用于用户的设备。在本文中，我们基于学习的 RTM 方案在计算上很简单，但在恢复丢失的细节方面很有效。为了学习 SDR 到 HDR 的关系，首先通过应用引导过滤器将训练“SDR-HDR”图像分为基础层组件和细节层组件。“SDR-HDR”对的细节层组件用于训练 SDR 到 HDR 的映射。映射矩阵是基于核岭回归计算的。同时，基础层的全局对比度通过非线性函数进行扩展，该函数抑制较暗区域并放大较亮区域，以适应目标 HDR 显示器的完整 DR。为了验证我们基于学习的 RTM 方案的有效性，我们对图像和视频进行了主观质量评估。实验结果表明，我们的 RTM 方案优于现有的 RTM 方案，成功恢复了 SDR 图像中丢失的细节。