We propose a new Generative Adversarial Network for Compressed Video quality
Enhancement (CVEGAN). The CVEGAN generator benefits from the use of a novel
Mul2Res block (with multiple levels of residual learning branches), an enhanced
residual non-local block (ERNB) and an enhanced convolutional block attention
module (ECBAM). The ERNB has also been employed in the discriminator to improve
the representational capability. The training strategy has also been
re-designed specifically for video compression applications, to employ a
relativistic sphere GAN (ReSphereGAN) training methodology together with new
perceptual loss functions. The proposed network has been fully evaluated in the
context of two typical video compression enhancement tools: post-processing
(PP) and spatial resolution adaptation (SRA). CVEGAN has been fully integrated
into the MPEG HEVC video coding test model (HM16.20) and experimental results
demonstrate significant coding gains (up to 28% for PP and 38% for SRA compared
to the anchor) over existing state-of-the-art architectures for both coding
tools across multiple datasets.

本研究提出了一种新的生成对抗神经网络模型（CVEGAN），其应用在视频压缩质量增强方面，通过采用新颖的 Mul2Res 块、增强残差非局部块（ERNB）和增强的卷积块注意模块（ECBAM），以及重新设计的培训策略和感知损失函数来实现。CVEGAN 在现有多个数据集上表现出显著的编码性能提升。