The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this paper, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize one space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and the transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that the TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness across various scenarios.

翻译：客观点云质量评估（PCQA）研究旨在开发能够以感知一致的方式度量点云质量的量化指标。本文融合认知科学研究与人类视觉系统（HVS）的直觉，通过度量将失真点云变换回其参考点云的复杂度来评估点云质量，实际应用中该复杂度可近似视为给定一点云时另一点云的编码长度。为此，我们首先基于三维Voronoi图对参考点云和失真点云进行空间分割，获得一系列局部块对。其次，受预测编码理论启发，我们利用空间感知向量自回归（SA-VAR）模型分别对有/无失真块参与下的每个参考块的几何通道和颜色通道进行编码。假设残差服从多元高斯分布，利用协方差矩阵计算参考样本的自复杂度以及参考样本与失真样本间的变换复杂度。此外，引入SA-VAR生成的预测项作为辅助特征以提升最终质量预测。通过在五个公开点云质量评估数据库上的大量实验，验证了所提出的基于变换复杂度的失真度量（TCDM）的有效性。结果表明TCDM达到了最先进的（SOTA）性能，进一步分析证实了其在多种场景下的鲁棒性。