A New Journey from SDRTV to HDRTV

TL;DR

This paper introduces a novel three-step pipeline and a lightweight network for converting SDRTV content to HDRTV, supported by a new dataset and state-of-the-art results in quality and performance.

Contribution

It proposes a new analysis, a three-step transformation pipeline, a lightweight adaptive network, and a new HDRTV dataset for SDR to HDR conversion.

Findings

Achieved state-of-the-art quantitative performance

Produced visually appealing HDR results

Developed a new HDRTV dataset HDRTV1K

Abstract

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.