Uncertainty-aware No-Reference Point Cloud Quality Assessment

• URL: http://arxiv.org/abs/2401.08926v1
• Date: Wed, 17 Jan 2024 02:25:42 GMT
• Title: Uncertainty-aware No-Reference Point Cloud Quality Assessment
• Authors: Songlin Fan, Zixuan Guo, Wei Gao, Ge Li
• Abstract summary: This work presents the first probabilistic architecture for no-reference point cloud quality assessment (PCQA) The proposed method can model the quality judgingity of subjects through a tailored conditional variational autoencoder (AE) Experiments indicate that our approach mimics previous cutting-edge methods by a large margin and exhibits cross-dataset experiments.
• Score: 25.543217625958462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of compression and enhancement algorithms necessitates an accurate quality assessment for point clouds. Previous works consistently regard point cloud quality assessment (PCQA) as a MOS regression problem and devise a deterministic mapping, ignoring the stochasticity in generating MOS from subjective tests. Besides, the viewpoint switching of 3D point clouds in subjective tests reinforces the judging stochasticity of different subjects compared with traditional images. This work presents the first probabilistic architecture for no-reference PCQA, motivated by the labeling process of existing datasets. The proposed method can model the quality judging stochasticity of subjects through a tailored conditional variational autoencoder (CVAE) and produces multiple intermediate quality ratings. These intermediate ratings simulate the judgments from different subjects and are then integrated into an accurate quality prediction, mimicking the generation process of a ground truth MOS. Specifically, our method incorporates a Prior Module, a Posterior Module, and a Quality Rating Generator, where the former two modules are introduced to model the judging stochasticity in subjective tests, while the latter is developed to generate diverse quality ratings. Extensive experiments indicate that our approach outperforms previous cutting-edge methods by a large margin and exhibits gratifying cross-dataset robustness.

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