CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement
This work addresses the problem of improving compressed video quality for viewers by reducing artifacts and enhancing visual fidelity, offering substantial improvements over current methods.
This paper introduces CVEGAN, a Generative Adversarial Network designed to enhance the quality of compressed video. It achieves significant coding gains, up to 28% for post-processing and 38% for spatial resolution adaptation, compared to existing state-of-the-art architectures.
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.