As a new form of volumetric media, Light Field (LF) can provide users with a true six degrees of freedom immersive experience because LF captures the scene with photo-realism, including aperture-limited changes in viewpoint. But uncompressed LF data is too large for network transmission, which is the reason why LF compression has become an important research topic. One of the more recent approaches for LF compression is to reduce the angular resolution of the input LF during compression and to use LF reconstruction to recover the discarded viewpoints during decompression. Following this approach, we propose a new LF reconstruction algorithm based on Graph Neural Networks; we show that it can achieve higher compression and better quality compared to existing reconstruction methods, although suffering from the same problem as those methods—the inability to deal effectively with high-frequency image components. To solve this problem, we propose an adaptive two-layer compression architecture that separates high-frequency and low-frequency components and compresses each with a different strategy so that the performance can become robust and controllable. Experiments with multiple datasets 1 show that our proposed scheme is capable of providing a decompression quality of above 40 dB, and can significantly improve compression efficiency compared with similar LF reconstruction schemes.

中文翻译：

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一种基于 GNN 重构的自适应两层光场压缩方案

作为一种新的体积媒体形式，光场 (LF) 可以为用户提供真正的六自由度沉浸式体验，因为 LF 以照片般逼真的方式捕捉场景，包括视点的光圈限制变化。但未压缩的LF数据对于网络传输来说太大了，这也是LF压缩成为重要研究课题的原因。LF 压缩的最新方法之一是在压缩期间降低输入 LF 的角分辨率，并在解压缩期间使用 LF 重建来恢复丢弃的视点。按照这种方法，我们提出了一种新的基于图神经网络的LF重建算法；我们表明，与现有的重建方法相比，它可以实现更高的压缩和更好的质量，尽管存在与这些方法相同的问题——无法有效处理高频图像分量。为了解决这个问题，我们提出了一种自适应的两层压缩架构，将高频和低频分量分开，并用不同的策略进行压缩，从而使性能变得鲁棒和可控。使用多个数据集进行实验1表明我们提出的方案能够提供 40 dB 以上的解压缩质量，并且与类似的 LF 重建方案相比，可以显着提高压缩效率。