  This paper describes a CNN-based multi-frame post-processing approach based
on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN.
This method has been integrated with the Versatile Video Coding Test Model
(VTM) 15.2 to enhance the visual quality of the final reconstructed content.
The evaluation results on the CLIC 2022 validation sequences show consistent
coding gains over the original VVC VTM at the same bitrates when assessed by
PSNR. The integrated codec has been submitted to the Challenge on Learned Image
Compression (CLIC) 2022 (video track), and the team name associated with this
submission is BVI_VC.
