The rise of HDR-WCG display devices has highlighted the need to convert SDRTV to HDRTV, as most video sources are still in SDR. Existing methods primarily focus on designing neural networks to learn a single-style mapping from SDRTV to HDRTV. However, the limited information in SDRTV and the diversity of styles in real-world conversions render this process an ill-posed problem, thereby constraining the performance and generalization of these methods. Inspired by generative approaches, we propose a novel method for SDRTV to HDRTV conversion guided by real HDRTV priors. Despite the limited information in SDRTV, introducing real HDRTV as reference priors significantly constrains the solution space of the originally high-dimensional ill-posed problem. This shift transforms the task from solving an unreferenced prediction problem to making a referenced selection, thereby markedly enhancing the accuracy and reliability of the conversion process. Specifically, our approach comprises two stages: the first stage employs a Vector Quantized Generative Adversarial Network to capture HDRTV priors, while the second stage matches these priors to the input SDRTV content to recover realistic HDRTV outputs. We evaluate our method on public datasets, demonstrating its effectiveness with significant improvements in both objective and subjective metrics across real and synthetic datasets.

翻译：随着HDR-WCG显示设备的兴起，将SDRTV转换为HDRTV的需求日益凸显，因为大多数视频源仍为SDR格式。现有方法主要集中于设计神经网络以学习从SDRTV到HDRTV的单一样式映射。然而，SDRTV中信息的有限性以及真实世界转换中风格的多样性，使得该过程成为一个不适定问题，从而限制了这些方法的性能与泛化能力。受生成式方法的启发，我们提出了一种由真实HDRTV先验引导的SDRTV到HDRTV转换新方法。尽管SDRTV信息有限，引入真实HDRTV作为参考先验能显著约束原本高维不适定问题的解空间。这一转变将任务从解决无参考的预测问题转化为有参考的选择问题，从而显著提升了转换过程的准确性与可靠性。具体而言，我们的方法包含两个阶段：第一阶段采用矢量量化生成对抗网络捕获HDRTV先验，第二阶段将这些先验与输入的SDRTV内容进行匹配以恢复真实的HDRTV输出。我们在公开数据集上评估了所提方法，结果表明其在真实与合成数据集上均能通过客观与主观指标的显著提升有效验证方法的优越性。