Point Cloud Quality Assessment Using Multi-Level Features

Recently, point clouds have emerged as a promising research direction for representing 3D visual data in various immersive applications, including augmented reality, self-driving cars and so on. The research on point cloud quality assessment (PCQA) has received significant attention. Although there are various objective PCQA models, their generalization can not satisfy the requirement of practical applications. Obviously, various factors should be considered comprehensively in the PCQA model. The dependence of data and the pertinence of features limit the improvement of the generalization of the PCQA models. Extracting features from different types of point clouds and research directions is also a challenge. To overcome this limitation, we propose a multi-level features model (MFPCQA) that segregates the basic features from the point cloud data into a pool. Then, the extracted basic features are sorted and combined to generate the advanced features, which are more closely related to quality, obtaining a multi-level feature structure. Finally, an efficient quality regressor maps these advanced features to point cloud quality. It is worth noting that, according to different application scenarios, our proposed MFPCQA has carried out detailed experiments on two conditions of whether it can take advantage of the distortion type. Extensive experiments on several publicly available subjective point cloud quality datasets and different distortion type datas validate that our proposed MFPCQA can compete with state-of-the-art full-reference, reduced-reference quality assessment models. The proposed MFPCQA significantly improves the generalization of quality assessment algorithms.