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-广色域)电视但大多数成品仍为SDR(标准动态范围)时,需要SDR到HDRTV上采样。研究社区已经开始通过基于学习的方法解决这个低级视觉任务。然而,当应用于真实的SDR时,现有方法倾向于产生昏暗和失真的结果,几乎没有改进观看体验。与其他网络导向的方法不同,我们将这种缺陷归因于训练集(HDR-SDR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。然后,将其用于训练亮度分段网络(LSN),包括全局映射主干和两个亮度范围的Transformer分支。我们还通过度量标准和主观实验更新评估标准。最后,进行了消融研究以证明其有效性。我们的工作可在以下网址获得:https://github.com/AndreGuo/HDRTVDM。