  Video compression artifact reduction aims to recover high-quality videos from
low-quality compressed videos. Most existing approaches use a single
neighboring frame or a pair of neighboring frames (preceding and/or following
the target frame) for this task. Furthermore, as frames of high quality overall
may contain low-quality patches, and high-quality patches may exist in frames
of low quality overall, current methods focusing on nearby peak-quality frames
(PQFs) may miss high-quality details in low-quality frames. To remedy these
shortcomings, in this paper we propose a novel end-to-end deep neural network
called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple
consecutive frames. An approximate non-local strategy is introduced in
NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal
dependency in a video sequence. This approximate strategy makes the non-local
module work in a fast and low space-cost way. Our method uses the preceding and
following frames of the target frame to generate a residual, from which a
higher quality frame is reconstructed. Experiments on two datasets show that
NL-ConvLSTM outperforms the existing methods.
