In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://github.com/AndreGuo/HDRTVDM.

翻译：在媒体行业中，当用户拥有HDR-WCG（高动态范围-宽色域）电视而大多数现成视频素材仍处于SDR（标准动态范围）时，便产生了SDR到HDRTV上转换的需求。研究界已开始通过基于学习的方法解决这一低级视觉任务。然而，当应用于真实SDR内容时，现有方法往往生成暗淡且去饱和的结果，几乎无法提升观看体验。不同于其他面向网络的方法，我们将这种缺陷归因于训练集（HDR-SDR配对）。因此，我们提出了新的HDRTV数据集（称为HDRTV4K）和新的HDR到SDR退化模型。随后，该数据集被用于训练一个亮度分段网络（LSN），该网络包含一个全局映射主干，以及两个分别处理亮和暗亮度范围的Transformer分支。我们还通过定制化指标和主观实验更新了评估标准。最后，通过消融研究验证了方法的有效性。我们的代码可在以下地址获取：https://github.com/AndreGuo/HDRTVDM。