  For video super-resolution, current state-of-the-art approaches either
process multiple low-resolution (LR) frames to produce each output
high-resolution (HR) frame separately in a sliding window fashion or
recurrently exploit the previously estimated HR frames to super-resolve the
following frame. The main weaknesses of these approaches are: 1) separately
generating each output frame may obtain high-quality HR estimates while
resulting in unsatisfactory flickering artifacts, and 2) combining previously
generated HR frames can produce temporally consistent results in the case of
short information flow, but it will cause significant jitter and jagged
artifacts because the previous super-resolving errors are constantly
accumulated to the subsequent frames. In this paper, we propose a fully
end-to-end trainable frame and feature-context video super-resolution (FFCVSR)
network that consists of two key sub-networks: local network and context
network, where the first one explicitly utilizes a sequence of consecutive LR
frames to generate local feature and local SR frame, and the other combines the
outputs of local network and the previously estimated HR frames and features to
super-resolve the subsequent frame. Our approach takes full advantage of the
inter-frame information from multiple LR frames and the context information
from previously predicted HR frames, producing temporally consistent
high-quality results while maintaining real-time speed by directly reusing
previous features and frames. Extensive evaluations and comparisons demonstrate
that our approach produces state-of-the-art results on a standard benchmark
dataset, with advantages in terms of accuracy, efficiency, and visual quality
over the existing approaches.
