论文标题
通过基于深度学习的多帧后处理来增强VVC
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing
论文作者
论文摘要
本文介绍了一种基于CNN的多帧后处理方法，基于感知启发的生成对抗网络架构Cvegan。该方法已与多功能视频编码测试模型（VTM）15.2集成，以增强最终重建内容的视觉质量。通过PSNR评估，对CLIC 2022验证序列的评估结果显示，在相同比特率的原始VVC VTM上的编码增长一致。集成的编解码器已提交给学习的图像压缩（CLIC）2022（视频轨道）的挑战，与此提交相关的团队名称为BVI_VC。
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.
