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 protected]. 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.

翻译：视频超分辨率(VSR)技术在重建低质量视频方面十分出色,避免了基于内推的算法造成的不愉快的模糊效应;然而,巨大的计算复杂性和记忆占用妨碍了实际应用中的易腐性边缘和运行时间推断,特别是对于大规模VSR任务而言。本文探讨了实时VSR系统的可能性,并设计了一个高效和通用VSR网络,称为EGVSR。拟议的EGVSR基于在时间一致性方面的对立学习。为了追求更快的 VSR处理能力,直至4K分辨率,本文试图选择较轻的网络结构和高效的升级方法,以减少EGVSR网络在高视觉质量的保证下所需的计算。此外,我们还在实际硬件平台上实施批次正常计算、振动加速算和其他神经网络加速技术,以优化EGVSR网络的推力进程。最后,我们EGVSR在4的高级处理能力上找到了[email protected]。 比较总体性能结构网络的升级和图像SR的升级。