TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment

TL;DR

This paper introduces TCDM, a novel perceptual point cloud quality metric based on transformational complexity, which measures the effort to reconstruct a reference point cloud from a distorted version using a space-aware predictive coding approach.

Contribution

The paper proposes a new quality assessment metric that combines space segmentation and predictive coding theory to evaluate point cloud quality more perceptually accurately.

Findings

TCDM achieves state-of-the-art performance on multiple datasets.

The method demonstrates robustness across various distortion scenarios.

The approach effectively correlates with human perceptual judgments.

Abstract

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 a 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 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 TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios. The code is publicly available at https://github.com/zyj1318053/TCDM.