Inevitable compression artifacts in multi-view video (MVV) can clearly degrade the quality of experience in many interaction-oriented 3D visual applications. Under the framework of asymmetric coding, low-quality images can be enhanced with high-quality images from the neighboring viewpoints considering the similarity among different views. However, compression artifacts and warping error cause different cross-view quality gaps for various sequences, and thus the contribution of cross-view priors can hardly be located and considered in previous works. In this paper, we propose a multi-view graph neural network (MV-GNN) to reduce compression artifacts in multi-view compressed images. We dedicate to design a fusion mechanism which can exploit contributions from neighboring viewpoints and meanwhile suppress the misleading information. In our method, a GNN-based fusion mechanism is designed to fuse the cross-view information under the aggregation and update mechanism of GNN. Experiments show that 1.672 dB and 0.0242 average gains on PSNR and SSIM metrics can be obtained, respectively. For the subjective evaluations, blocking effect in the compressed images are clearly suppressed and the damaged object boundary are better recovered. The experimental results demonstrate that our MV-GNN outperforms the state-of-the-art methods.

中文翻译：

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MV-GNN：多视图图形神经网络，可减少压缩伪像

多视图视频（MVV）中不可避免的压缩伪影会明显降低许多面向交互的3D视觉应用程序中的体验质量。在非对称编码的框架下，考虑到不同视图之间的相似性，可以从相邻视点使用高质量图像增强低质量图像。但是，压缩伪影和翘曲误差会导致不同序列的不同横断面质量差距，因此，在先前的工作中很难找到和考虑横断先验的贡献。在本文中，我们提出了一种多视图图神经网络（MV-GNN），以减少多视图压缩图像中的压缩伪像。我们致力于设计一种融合机制，该机制可以利用邻近观点的贡献，同时抑制误导性信息。用我们的方法 设计了一种基于GNN的融合机制，在GNN的聚集和更新机制下融合交叉视图信息。实验表明，分别在PSNR和SSIM指标上可获得1.672 dB和0.0242的平均增益。为了进行主观评估，可以明显抑制压缩图像中的遮挡效果，并且可以更好地恢复损坏的对象边界。实验结果表明，我们的MV-GNN优于最新方法。清晰地抑制了压缩图像中的块效应，并更好地恢复了损坏的对象边界。实验结果表明，我们的MV-GNN优于最新方法。清晰地抑制了压缩图像中的块效应，并更好地恢复了损坏的对象边界。实验结果表明，我们的MV-GNN优于最新方法。