论文标题
基于模型的基于视频的3D点云压缩的几何和颜色之间的关节位分配
Model-based Joint Bit Allocation between Geometry and Color for Video-based 3D Point Cloud Compression
论文作者
论文摘要
费率失真优化在图像/视频编码中起着非常重要的作用。但是对于3D点云，尚未研究此问题。在本文中，详细研究了3D点云的速率和失真特性，并为3D点云解决了典型且具有挑战性的失真优化问题。具体而言，由于重建的3D点云的质量取决于几何和颜色扭曲，因此我们首先提出了基于视频的3D点云压缩平台中的几何形状和颜色信息的分析速率和失真模型，然后解决基于派生模型的几何和颜色的关节位分配问题。为了最大化3D点云的重建质量，位分配问题被提出为约束优化问题，并通过内部点方法解决。实验结果表明，所提出的解决方案的速率分数性能接近通过详尽搜索获得的速率，但仅占其时间复杂性的0.68％。此外，提出的速率和失真模型也可以用于其他速率延伸优化问题（例如预测模式决策）和未来3D点云编码的费率控制技术。
Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint bit allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the bit allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future.
