With the increasing consumption of 3D displays and virtual reality,
multi-view video has become a promising format. However, its high resolution
and multi-camera shooting result in a substantial increase in data volume,
making storage and transmission a challenging task. To tackle these
difficulties, we propose an implicit-explicit integrated representation for
multi-view video compression. Specifically, we first use the explicit
representation-based 2D video codec to encode one of the source views.
Subsequently, we propose employing the implicit neural representation
(INR)-based codec to encode the remaining views. The implicit codec takes the
time and view index of multi-view video as coordinate inputs and generates the
corresponding implicit reconstruction frames.To enhance the compressibility, we
introduce a multi-level feature grid embedding and a fully convolutional
architecture into the implicit codec. These components facilitate
coordinate-feature and feature-RGB mapping, respectively. To further enhance
the reconstruction quality from the INR codec, we leverage the high-quality
reconstructed frames from the explicit codec to achieve inter-view
compensation. Finally, the compensated results are fused with the implicit
reconstructions from the INR to obtain the final reconstructed frames. Our
proposed framework combines the strengths of both implicit neural
representation and explicit 2D codec. Extensive experiments conducted on public
datasets demonstrate that the proposed framework can achieve comparable or even
superior performance to the latest multi-view video compression standard MIV
and other INR-based schemes in terms of view compression and scene modeling.

通过隐式 - 显式混合编码和多层特征网格嵌入，我们提出了一种用于多视点视频压缩的方法，实验证明该方法在视点压缩和场景建模方面取得了与最新的多视点视频压缩标准 MIV 和其他基于隐式神经表示的方案相当甚至更好的性能。