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.

翻译：随着3D显示和虚拟现实的日益普及，多视点视频已成为一种具有前景的格式。然而，其高分辨率和多摄像头拍摄导致数据量大幅增加，使存储和传输成为一项艰巨任务。为应对这些挑战，我们提出了一种用于多视点视频压缩的隐式-显式集成表示。具体而言，我们首先使用基于显式表示的2D视频编解码器对其中一个源视点进行编码。随后，我们提出采用基于隐式神经表示（INR）的编解码器对剩余视点进行编码。隐式编解码器将多视点视频的时间和视点索引作为坐标输入，并生成相应的隐式重建帧。为增强可压缩性，我们在隐式编解码器中引入了多级特征网格嵌入和全卷积架构。这些组件分别促进坐标-特征映射和特征-RGB映射。为进一步提升INR编解码器的重建质量，我们利用显式编解码器获得的高质量重建帧实现视点间补偿。最后，将补偿结果与INR的隐式重建结果相融合，得到最终重建帧。我们提出的框架融合了隐式神经表示与显式2D编解码器的双重优势。在公开数据集上进行的大量实验表明，所提框架在视点压缩和场景建模方面能够达到甚至超越最新多视点视频压缩标准MIV及其他基于INR方案的性能。