  The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual
quality of videos, by simultaneously performing video frame interpolation (VFI)
and video super-resolution (VSR). However, facing the challenge of the
additional temporal dimension and scale inconsistency, most existing STVSR
methods are complex and inflexible in dynamically modeling different motion
amplitudes. In this work, we find that choosing an appropriate processing scale
achieves remarkable benefits in flow-based feature propagation. We propose a
novel Scale-Adaptive Feature Aggregation (SAFA) network that adaptively selects
sub-networks with different processing scales for individual samples.
Experiments on four public STVSR benchmarks demonstrate that SAFA achieves
state-of-the-art performance. Our SAFA network outperforms recent
state-of-the-art methods such as TMNet and VideoINR by an average improvement
of over 0.5dB on PSNR, while requiring less than half the number of parameters
and only 1/3 computational costs.
