  This paper explores an efficient solution for Space-time Super-Resolution,
aiming to generate High-resolution Slow-motion videos from Low Resolution and
Low Frame rate videos. A simplistic solution is the sequential running of Video
Super Resolution and Video Frame interpolation models. However, this type of
solutions are memory inefficient, have high inference time, and could not make
the proper use of space-time relation property. To this extent, we first
interpolate in LR space using quadratic modeling. Input LR frames are
super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps
and blending mask which are used to synthesize LR interpolated frame is reused
in HR space using bilinear upsampling. This leads to a coarse estimate of HR
intermediate frame which often contains artifacts along motion boundaries. We
use a refinement network to improve the quality of HR intermediate frame via
residual learning. Our model is lightweight and performs better than current
state-of-the-art models in REDS STSR Validation set.
