  Joint super-resolution and inverse tone-mapping (SR-ITM) aims to enhance the
visual quality of videos that have quality deficiencies in resolution and
dynamic range. This problem arises when using 4K high dynamic range (HDR) TVs
to watch a low-resolution standard dynamic range (LR SDR) video. Previous
methods that rely on learning local information typically cannot do well in
preserving color conformity and long-range structural similarity, resulting in
unnatural color transition and texture artifacts. In order to tackle these
challenges, we propose a global priors guided modulation network (GPGMNet) for
joint SR-ITM. In particular, we design a global priors extraction module (GPEM)
to extract color conformity prior and structural similarity prior that are
beneficial for ITM and SR tasks, respectively. To further exploit the global
priors and preserve spatial information, we devise multiple global priors
guided spatial-wise modulation blocks (GSMBs) with a few parameters for
intermediate feature modulation, in which the modulation parameters are
generated by the shared global priors and the spatial features map from the
spatial pyramid convolution block (SPCB). With these elaborate designs, the
GPGMNet can achieve higher visual quality with lower computational complexity.
Extensive experiments demonstrate that our proposed GPGMNet is superior to the
state-of-the-art methods. Specifically, our proposed model exceeds the
state-of-the-art by 0.64 dB in PSNR, with 69$\%$ fewer parameters and
3.1$\times$ speedup. The code will be released soon.
