  Online processing of compressed videos to increase their resolutions attracts
increasing and broad attention. Video Super-Resolution (VSR) using recurrent
neural network architecture is a promising solution due to its efficient
modeling of long-range temporal dependencies. However, state-of-the-art
recurrent VSR models still require significant computation to obtain a good
performance, mainly because of the complicated motion estimation for
frame/feature alignment and the redundant processing of consecutive video
frames. In this paper, considering the characteristics of compressed videos, we
propose a Codec Information Assisted Framework (CIAF) to boost and accelerate
recurrent VSR models for compressed videos. Firstly, the framework reuses the
coded video information of Motion Vectors to model the temporal relationships
between adjacent frames. Experiments demonstrate that the models with Motion
Vector based alignment can significantly boost the performance with negligible
additional computation, even comparable to those using more complex optical
flow based alignment. Secondly, by further making use of the coded video
information of Residuals, the framework can be informed to skip the computation
on redundant pixels. Experiments demonstrate that the proposed framework can
save up to 70% of the computation without performance drop on the REDS4 test
videos encoded by H.264 when CRF is 23.
