Multi-view video plus depth (MVD) is the promising and widely adopted data representation for future 3D visual applications and interactive media. However, compression distortions on depth videos impede the development of such applications, and filters are crucially needed for the quality enhancement at the terminal side. Cross-view priors can intuitively be involved in filter design, but these priors are also distorted in compression and thus the contribution of them can hardly be considered in previous research. In this article, we propose a cross-view optimized filter for depth map quality enhancement by making full use of inner- and cross-view priors. We dedicate to evaluate the contributions of distorted cross-view priors in filtering the current view of depth, and then both inner- and cross-view priors can be involved in the filter design. Thus, distortions of cross-view priors are not barriers again as before. For the purpose of that, mutual information guided cross-view consistency is designed to evaluate the contributions of cross-view priors from compression distortions of MVD. After that, under the framework of global optimization, both inner- and cross-view priors are modeled and taken to minimize the designed energy function where both data accuracy and spatial smoothness are modeled. The experimental results show that the proposed model outperforms state-of-the-art methods, where 3.289 dB and 0.0407 average gains on peak signal-to-noise ratio and structural similarity metrics can be obtained, respectively. For the subjective evaluations, object details and structure information are recovered in the compressed depth video. We also verify our method via several practical applications, including virtual view synthesis for smooth interaction and point cloud for 3D modeling for accuracy evaluation. In these verifications, the ringing and malposition artifacts on object contours are properly handled for interactive video, and discontinuous object surfaces are restored for 3D modeling. All of these results suggest that compression distortions in MVD can be properly filtered by the proposed model, which provides a promising solution for future bandwidth constrained 3D and interactive visual applications.

中文翻译：

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充分利用先验

多视图视频加深度 (MVD) 是用于未来 3D 视觉应用和交互式媒体的有前途且被广泛采用的数据表示。然而，深度视频的压缩失真阻碍了此类应用的发展，而滤波器对于终端侧的质量增强至关重要。交叉视图先验可以直观地参与滤波器设计，但这些先验在压缩中也存在失真，因此在以前的研究中很难考虑它们的贡献。在本文中，我们通过充分利用内部和交叉视图先验，提出了一种用于深度图质量增强的交叉视图优化滤波器。我们致力于评估扭曲的交叉视图先验在过滤当前深度视图中的贡献，然后内部和交叉视图先验都可以参与过滤器设计。因此，交叉视图先验的扭曲不再像以前那样成为障碍。为此，互信息引导的跨视图一致性旨在评估跨视图先验对 MVD 压缩失真的贡献。之后，在全局优化的框架下，对内部和交叉视图先验进行建模并采用以最小化设计的能量函数，其中对数据准确性和空间平滑度进行建模。实验结果表明，所提出的模型优于最先进的方法，其中峰值信噪比和结构相似性指标的平均增益分别为 3.289 dB 和 0.0407。对于主观评价，在压缩深度视频中恢复对象细节和结构信息。我们还通过几个实际应用验证了我们的方法，包括用于平滑交互的虚拟视图合成和用于 3D 建模以进行精度评估的点云。在这些验证中，对象轮廓上的振铃和错位伪影被正确处理以用于交互式视频，并且不连续的对象表面被恢复以用于 3D 建模。所有这些结果表明，MVD 中的压缩失真可以通过所提出的模型进行适当过滤，这为未来带宽受限的 3D 和交互式视觉应用提供了一个有前途的解决方案。和不连续的对象表面恢复为 3D 建模。所有这些结果表明，MVD 中的压缩失真可以通过所提出的模型进行适当过滤，这为未来带宽受限的 3D 和交互式视觉应用提供了一个有前途的解决方案。和不连续的对象表面恢复为 3D 建模。所有这些结果表明，MVD 中的压缩失真可以通过所提出的模型进行适当过滤，这为未来带宽受限的 3D 和交互式视觉应用提供了一个有前途的解决方案。