Su Honglei, Liu Qi, Yuan Hui, Cheng Qiang, Hamzaoui Raouf
College of Electronics and Information, Qingdao University, Qingdao 266071, China.
College of Electronics and Information, Qingdao University, Qingdao 266237, China.
IEEE Trans Multimedia. 2024;26:6238-6249. doi: 10.1109/tmm.2023.3347638. Epub 2023 Dec 27.
Video-based point cloud compression (V-PCC) is a state-of-the-art moving picture experts group (MPEG) standard for point cloud compression. V-PCC can be used to compress both static and dynamic point clouds in a lossless, near lossless, or lossy way. Many objective quality metrics have been proposed for distorted point clouds. Most of these metrics are full-reference metrics that require both the original point cloud and the distorted one. However, in some real-time applications, the original point cloud is not available, and no-reference or reduced-reference quality metrics are needed. Three main challenges in the design of a reduced-reference quality metric are how to build a set of features that characterize the visual quality of the distorted point cloud, how to select the most effective features from this set, and how to map the selected features to a perceptual quality score. We address the first challenge by proposing a comprehensive set of features consisting of compression, geometry, normal, curvature, and luminance features. To deal with the second challenge, we use the least absolute shrinkage and selection operator (LASSO) method, which is a variable selection method for regression problems. Finally, we map the selected features to the mean opinion score in a nonlinear space. Although we have used only 19 features in our current implementation, our metric is flexible enough to allow any number of features, including future more effective ones. Experimental results on the Waterloo point cloud dataset version 2 (WPC2.0) and the MPEG point cloud compression dataset (M-PCCD) show that our method, namely PCQAML, outperforms state-of-the-art full-reference and reduced-reference quality metrics in terms of Pearson linear correlation coefficient, Spearman rank order correlation coefficient, Kendall's rank-order correlation coefficient, and root mean squared error.
基于视频的点云压缩（V-PCC）是一种用于点云压缩的先进的运动图像专家组（MPEG）标准。V-PCC可用于以无损、近无损或有损方式压缩静态和动态点云。针对失真的点云已经提出了许多客观质量指标。这些指标中的大多数是全参考指标，需要原始点云和失真点云两者。然而，在一些实时应用中，原始点云不可用，因此需要无参考或简化参考质量指标。简化参考质量指标设计中的三个主要挑战是如何构建一组表征失真点云视觉质量的特征，如何从该集合中选择最有效的特征，以及如何将所选特征映射到感知质量得分。我们通过提出一组由压缩、几何、法线、曲率和亮度特征组成的综合特征来解决第一个挑战。为了应对第二个挑战，我们使用最小绝对收缩和选择算子（LASSO）方法，这是一种用于回归问题的变量选择方法。最后，我们将所选特征映射到非线性空间中的平均意见得分。尽管我们在当前实现中仅使用了19个特征，但我们的指标足够灵活，可以允许任意数量的特征，包括未来更有效的特征。在滑铁卢点云数据集版本2（WPC2.0）和MPEG点云压缩数据集（M-PCCD）上的实验结果表明，我们的方法，即PCQAML，在皮尔逊线性相关系数、斯皮尔曼等级相关系数、肯德尔等级相关系数和均方根误差方面优于现有的全参考和简化参考质量指标。
