  In video compression, most of the existing deep learning approaches
concentrate on the visual quality of a single frame, while ignoring the useful
priors as well as the temporal information of adjacent frames. In this paper,
we propose a multi-frame guided attention network (MGANet) to enhance the
quality of compressed videos. Our network is composed of a temporal encoder
that discovers inter-frame relations, a guided encoder-decoder subnet that
encodes and enhances the visual patterns of target frame, and a
multi-supervised reconstruction component that aggregates information to
predict details. We design a bidirectional residual convolutional LSTM unit to
implicitly discover frames variations over time with respect to the target
frame. Meanwhile, the guided map is proposed to guide our network to
concentrate more on the block boundary. Our approach takes advantage of
intra-frame prior information and inter-frame information to improve the
quality of compressed video. Experimental results show the robustness and
superior performance of the proposed method.Code is available at
https://github.com/mengab/MGANet
