  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.
