通过图像形成过程学习实现高效的SDRTV到HDRTV转换
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
AI总结：
本文提出HDRTVNet++，通过建模图像形成过程，以自适应全局颜色映射、局部增强和高光细化三步法，高效实现SDR到HDR转换，并在4K数据集HDRTV1K上取得最先进性能。
摘要：
现代显示器可以渲染高动态范围（HDR）和宽色域（WCG）的视频内容。然而，大多数资源仍然是标准动态范围（SDR）。因此，将现有的SDR内容转换为HDRTV标准具有重要价值。本文通过建模SDRTV/HDRTV内容的形成过程来定义和分析SDRTV到HDRTV的任务。我们的研究发现，简单的端到端监督训练方法会遭受严重的色域转换错误。为了解决这个问题，我们提出了一种名为HDRTVNet++的新三步解决方案，包括自适应全局颜色映射、局部增强和高光细化。自适应全局颜色映射步骤利用全局统计信息进行图像自适应的颜色调整。局部增强网络进一步增强细节，两个子网络作为生成器组合，通过基于GAN的联合训练实现高光一致性。我们的方法专为超高清电视内容设计，在处理4K分辨率图像时既有效又轻量。我们还使用HDR10标准的HDR视频构建了一个数据集，命名为HDRTV1K，包含1235张训练图像和117张测试图像，均为4K分辨率。此外，我们采用五个指标来评估SDRTV到HDRTV的性能。我们的结果在定量和视觉上都展示了最先进的性能。代码和模型可在https://github.com/xiaom233/HDRTVNet-plus获取。
英文摘要：
Modern displays can render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most resources are still in standard dynamic range (SDR). Therefore, transforming existing SDR content into the HDRTV standard holds significant value. This paper defines and analyzes the SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Our findings reveal that a naive endto-end supervised training approach suffers from severe gamut transition errors. To address this, we propose a new three-step solution called HDRTVNet++, which includes adaptive global color mapping, local enhancement, and highlight refinement. The adaptive global color mapping step utilizes global statistics for image-adaptive color adjustments. A local enhancement network further enhances details, and the two sub-networks are combined as a generator to achieve highlight consistency through GANbased joint training. Designed for ultra-high-definition TV content, our method is both effective and lightweight for processing 4K resolution images. We also constructed a dataset using HDR videos in the HDR10 standard, named HDRTV1K, containing 1235 training and 117 testing images, all in 4K resolution. Additionally, we employ five metrics to evaluate SDRTV-to-HDRTV performance. Our results demonstrate state-of-the-art performance both quantitatively and visually. The codes and models are available at https://github.com/xiaom233/HDRTVNet-plus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
