Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network

Recently, the transformation of standard dynamic range TV (SDRTV) to high dynamic range TV (HDRTV) is in high demand due to the scarcity of HDRTV content. However, the conversion of SDRTV to HDRTV often amplifies the existing coding artifacts in SDRTV which deteriorate the visual quality of the output. In this study, we propose a dual inverse degradation SDRTV-to-HDRTV network DIDNet to address the issue of coding artifact restoration in converted HDRTV, which has not been previously studied. Specifically, we propose a temporal-spatial feature alignment module and dual modulation convolution to remove coding artifacts and enhance color restoration ability. Furthermore, a wavelet attention module is proposed to improve SDRTV features in the frequency domain. An auxiliary loss is introduced to decouple the learning process for effectively restoring from dual degradation. The proposed method outperforms the current state-of-the-art method in terms of quantitative results, visual quality, and inference times, thus enhancing the performance of the SDRTV-to-HDRTV method in real-world scenarios.

SDRTV-to-HDRTV Conversion via Spatial-Temporal Feature Fusion

HDR(High Dynamic Range) video can reproduce realistic scenes more realistically, with a wider gamut and broader brightness range. HDR video resources are still scarce, and most videos are still stored in SDR (Standard Dynamic Range) format. Therefore, SDRTV-to-HDRTV Conversion (SDR video to HDR video) can significantly enhance the user's video viewing experience. Since the correlation between adjacent video frames is very high, the method utilizing the information of multiple frames can improve the quality of the converted HDRTV. Therefore, we propose a multi-frame fusion neural network \textbf{DSLNet} for SDRTV to HDRTV conversion. We first propose a dynamic spatial-temporal feature alignment module \textbf{DMFA}, which can align and fuse multi-frame. Then a novel spatial-temporal feature modulation module \textbf{STFM}, STFM extracts spatial-temporal information of adjacent frames for more accurate feature modulation. Finally, we design a quality enhancement module \textbf{LKQE} with large kernels, which can enhance the quality of generated HDR videos. To evaluate the performance of the proposed method, we construct a corresponding multi-frame dataset using HDR video of the HDR10 standard to conduct a comprehensive evaluation of different methods. The experimental results show that our method obtains state-of-the-art performance. The dataset and code will be released.