MVAW-PCQA: a no-reference point cloud quality assessment via multi-view adaptive weighting

Point cloud quality assessment (PCQA) is a critical research area focused on evaluating the perceptual Quality of Experience (QoE) of point clouds to enhance visual experiences of immersive multimedia applications for end users. To prevent the complex computations on 3D data applied by model-based methods, projection-based models have been developed to estimate the QoE by analysing 2D projection views of the point cloud. In this paper, we propose a novel projection-based No-Reference (NR) PCQA method, called Multi-View Adaptive Weighting Point Cloud Quality Assessment (MVAW-PCQA), to predict the QoE of distorted point clouds using six 2D projection views as the input of a convolutional neural network (CNN) architecture. First, multi-view involves independently extracting features from multiple projection views of a point cloud, guaranteeing view-specific features are learned without prematurely mixing spatial information, and preserving the unique contributions of each projection view to the final quality prediction. Then, an adaptive weighting fusion mechanism combines the features extracted from the different projection views by learning their relative importance. This design enables the model to focus on the most informative projections for predicting the point cloud quality. The experimental results demonstrate that our method outperforms state-of-the-art NR-PCQA methods on the SJTU-PCQA dataset in terms of root mean square error (RMSE) and correlation coefficients (Pearson, Spearman, and Kendall), while adopting a lightweight design with a reasonable number of parameters for the trained neural network.