  Deep learning-based models have achieved remarkable performance in video
super-resolution (VSR) in recent years, but most of these models are less
applicable to online video applications. These methods solely consider the
distortion quality and ignore crucial requirements for online applications,
e.g., low latency and low model complexity. In this paper, we focus on online
video transmission, in which VSR algorithms are required to generate
high-resolution video sequences frame by frame in real time. To address such
challenges, we propose an extremely low-latency VSR algorithm based on a novel
kernel knowledge transfer method, named convolutional kernel bypass graft
(CKBG). First, we design a lightweight network structure that does not require
future frames as inputs and saves extra time costs for caching these frames.
Then, our proposed CKBG method enhances this lightweight base model by
bypassing the original network with ``kernel grafts'', which are extra
convolutional kernels containing the prior knowledge of external pretrained
image SR models. In the testing phase, we further accelerate the grafted
multi-branch network by converting it into a simple single-path structure.
Experiment results show that our proposed method can process online video
sequences up to 110 FPS, with very low model complexity and competitive SR
performance.
