FastHDRNet: A new efficient method for SDR-to-HDR Translation
Siyuan Tian Hao Wang Yiren Rong Junhao Wang Renjie Dai Zhengxiao He
Modern displays nowadays possess the capability to render video content with
a high dynamic range (HDR) and an extensive color gamut (WCG).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 network (DNN) based SDR(Standard dynamic range) to HDR
(High dynamic range) conversion methods outperform conventional methods, but
they are either too large to implement or generate some terrible artifacts. We
propose a neural network for SDRTV to HDRTV conversion, termed "FastHDRNet".
This network includes two parts, Adaptive Universal Color Transformation and
Local Enhancement.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 achieve state-of-the-art
performance in both quantitative comparisons and visual quality with a
lightweight structure and a enhanced infer speed.