  Modern displays nowadays possess the capability to render video content with
a high dynamic range (HDR) and an extensive color gamut .However, the majority
of available resources are still in standard dynamic range (SDR). Therefore, we
need to identify an effective methodology for this objective.The existing deep
neural networks (DNN) based SDR to HDR conversion methods outperforms
conventional methods, but they are either too large to implement or generate
some terrible artifacts. We propose a neural network for SDR to HDR conversion,
termed "FastHDRNet". This network includes two parts, Adaptive Universal Color
Transformation (AUCT) and Local Enhancement (LE). The architecture is designed
as a lightweight network that utilizes global statistics and local information
with super high efficiency. After the experiment, we find that our proposed
method achieves state-of-the-art performance in both quantitative comparisons
and visual quality with a lightweight structure and a enhanced infer speed.
