  Video super-resolution (VSR) technology excels in reconstructing low-quality
video, avoiding unpleasant blur effect caused by interpolation-based
algorithms. However, vast computation complexity and memory occupation hampers
the edge of deplorability and the runtime inference in real-life applications,
especially for large-scale VSR task. This paper explores the possibility of
real-time VSR system and designs an efficient and generic VSR network, termed
EGVSR. The proposed EGVSR is based on spatio-temporal adversarial learning for
temporal coherence. In order to pursue faster VSR processing ability up to 4K
resolution, this paper tries to choose lightweight network structure and
efficient upsampling method to reduce the computation required by EGVSR network
under the guarantee of high visual quality. Besides, we implement the batch
normalization computation fusion, convolutional acceleration algorithm and
other neural network acceleration techniques on the actual hardware platform to
optimize the inference process of EGVSR network. Finally, our EGVSR achieves
the real-time processing capacity of<EMAIL_ADDRESS>Compared with TecoGAN, the
most advanced VSR network at present, we achieve 85.04% reduction of
computation density and 7.92x performance speedups. In terms of visual quality,
the proposed EGVSR tops the list of most metrics (such as LPIPS, tOF, tLP,
etc.) on the public test dataset Vid4 and surpasses other state-of-the-art
methods in overall performance score. The source code of this project can be
found on https://github.com/Thmen/EGVSR.
