Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most available resources are still in standard dynamic range (SDR). Therefore, there is an urgent demand to transform existing SDR-TV contents into their HDR-TV versions. In this paper, we conduct an analysis of SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Base on the analysis, we propose a three-step solution pipeline including adaptive global color mapping, local enhancement and highlight generation. Moreover, the above analysis inspires us to present a lightweight network that utilizes global statistics as guidance to conduct image-adaptive color mapping. In addition, we construct a dataset using HDR videos in HDR10 standard, named HDRTV1K, and select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Furthermore, our final results achieve state-of-the-art performance in quantitative comparisons and visual quality. The code and dataset are available at https://github.com/chxy95/HDRTVNet.
翻译:目前,现代显示器能够提供高动态范围(HDR)和广彩色全方位(WCG)的视频内容,然而,大多数可用资源仍然处于标准动态范围(SDR),因此迫切需要将现有的SDR-TV内容转换成其HDR-TV版本,在本文中,我们通过模拟SDRTV/HDRTV内容的形成,对SDRTV-TV任务进行分析,根据分析,我们提议了一个三步解决办法管道,包括适应性的全球色彩制图、地方提升和亮相生成。此外,上述分析激励我们提出一个轻量的网络,利用全球统计数据指导进行图像适应性色谱制图。此外,我们还利用HRDR10标准中的HDR录象,名为HDRTV1K, 并选择了五种衡量SDRTVTV-HDRTV算法结果的指标。此外,我们的最后结果在定量比较和视觉质量方面达到了最先进的表现。代码和数据集可在https://github.com/chxy95/HDRDRTVNet上查阅。