Quality-Gated Convolutional LSTM for Enhancing Compressed Video
release_w5wilqhlsraexay4qsph3r6ezi
by
Ren Yang, Xiaoyan Sun, Mai Xu, Wenjun Zeng
2019
Abstract
The past decade has witnessed great success in applying deep learning to
enhance the quality of compressed video. However, the existing approaches aim
at quality enhancement on a single frame, or only using fixed neighboring
frames. Thus they fail to take full advantage of the inter-frame correlation in
the video. This paper proposes the Quality-Gated Convolutional Long Short-Term
Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully
exploit the advantageous information in a large range of frames. More
importantly, due to the obvious quality fluctuation among compressed frames,
higher quality frames can provide more useful information for other frames to
enhance quality. Therefore, we propose learning the "forget" and "input" gates
in the ConvLSTM cell from quality-related features. As such, the frames with
various quality contribute to the memory in ConvLSTM with different importance,
making the information of each frame reasonably and adequately used. Finally,
the experiments validate the effectiveness of our QG-ConvLSTM approach in
advancing the state-of-the-art quality enhancement of compressed video, and the
ablation study shows that our QG-ConvLSTM approach is learnt to make a
trade-off between quality and correlation when leveraging multi-frame
information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.
In text/plain
format
Archived Files and Locations
application/pdf 2.3 MB
file_zajwrr4oxvekrjysi6uerz652i
|
arxiv.org (repository) web.archive.org (webarchive) |
1903.04596v3
access all versions, variants, and formats of this works (eg, pre-prints)