论文标题
使用双歧视器生成对抗网络和VVC时间可扩展性的光场图像编码
Light Field Image Coding Using Dual Discriminator Generative Adversarial Network and VVC Temporal Scalability
论文作者
论文摘要
光场技术代表了提供高质量VR含量的可行途径。但是，这样的成像系统产生了大量数据，导致迫切需要LF图像压缩解决方案。在本文中，我们提出了一个基于视图合成的有效LF图像编码方案。与其传输所有LF视图，只有其中一些是编码和传输的，而其余视图则被删除。使用多功能视频编码（VVC）对传输视图进行编码，并用作参考视图，以合成解码器端的缺失视图。删除的视图是使用有效的双鉴别器GAN模型生成的。参考/删除视图的选择是使用基于VVC时间可扩展性的速率失真优化进行的。实验结果表明，所提出的方法提供了较高的编码性能并克服了最新的LF图像压缩解决方案。
Light field technology represents a viable path for providing a high-quality VR content. However, such an imaging system generates a high amount of data leading to an urgent need for LF image compression solution. In this paper, we propose an efficient LF image coding scheme based on view synthesis. Instead of transmitting all the LF views, only some of them are coded and transmitted, while the remaining views are dropped. The transmitted views are coded using Versatile Video Coding (VVC) and used as reference views to synthesize the missing views at decoder side. The dropped views are generated using the efficient dual discriminator GAN model. The selection of reference/dropped views is performed using a rate distortion optimization based on the VVC temporal scalability. Experimental results show that the proposed method provides high coding performance and overcomes the state-of-the-art LF image compression solutions.
