Reviving Standard-Dynamic-Range Videos for High-Dynamic-Range Devices: A Learning Paradigm with Hybrid Attention Mechanisms

With the prevalence of high dynamic range (HDR) display devices, the demand to convert existing standard dynamic range television (SDRTV) video content to their corresponding HDR television (HDRTV) counterpart is growing exponentially. Herein, we propose a two-stage learning paradigm with hybrid attention mechanisms to fully exploit spatial, channel-wise, and regional correlations for faithfully driving such conversion. Specifically, in the first domain mapping stage, the Depth-wise Self-Attention (DSA) and Global Calibration Layer (GCL) are proposed, which adaptively leverage feature intra-relationships to construct better scene representation and achieve engaging SDRTV-to-HDRTV transformation. In the second highlight generation stage, considering that the over-exposed regions potentially lead to detail loss which brings enormous challenges to the conversion, we propose a Regional Self-Attention (RSA) module to specifically restore missing highlights. Extensive experimental results on public databases show that our method outperforms state-of-the-art approaches in terms of different quality evaluation measures. © 2023 IEEE.