Representation Learning Optimization for 3D Point Cloud Quality Assessment Without Reference

Recent information and communication systems have employed 3D Point Cloud (PC) as an advanced geometrical representation modality for immersive applications. Like most multimedia data, PCs are often compressed for transmission and viewing purposes, which can impact the perceived quality. Developing robust and efficient objective quality metrics for PCs is still an open problem. In this paper, we propose an end-to-end deep approach for evaluating the perceptual effects of point cloud compression solutions without reference. Our approach focuses on leveraging the intrinsic point cloud characteristics to quantify the coding impairments from few distant randomly selected patches using supervised and unsupervised training strategies. To evaluate the performance of our method, two well-known datasets have been used. The results demonstrate the effectiveness and reliability of the proposed method compared to to state-of-the-art methods.