FastHDRNet: A new efficient method for SDR-to-HDR Translation
This addresses the need for efficient SDR-to-HDR conversion for modern displays, but it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of converting standard dynamic range (SDR) video to high dynamic range (HDR) by proposing FastHDRNet, a lightweight neural network that achieves state-of-the-art performance with improved inference speed.
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.