  In media industry, the demand of SDR-to-HDRTV up-conversion arises when users
possess HDR-WCG (high dynamic range-wide color gamut) TVs while most
off-the-shelf footage is still in SDR (standard dynamic range). The research
community has started tackling this low-level vision task by learning-based
approaches. When applied to real SDR, yet, current methods tend to produce dim
and desaturated result, making nearly no improvement on viewing experience.
Different from other network-oriented methods, we attribute such deficiency to
training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed
HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a
luminance-segmented network (LSN) consisting of a global mapping trunk, and two
Transformer branches on bright and dark luminance range. We also update
assessment criteria by tailored metrics and subjective experiment. Finally,
ablation studies are conducted to prove the effectiveness. Our work is
available at: https://github.com/AndreGuo/HDRTVDM.
