TGP-PCQA: Texture and geometry projection based quality assessment for colored point clouds

Abstract

Colored point cloud (PC) will inevitably encounter distortion during its acquisition, processing, coding and transmission, which may affect the visual quality of the colored PC. Therefore, it is necessary to design an effective tool for colored PC quality assessment (PCQA). In this paper, considering the mapping relationship of perception between the colored PC and its corresponding projection images, we propose a novel PCQA method based on texture and geometry projection (denoted as TGP-PCQA). The main idea of the proposed TGP-PCQA method is to obtain texture and geometry projection maps from different perspectives for evaluating the colored PC. Specifically, 4D tensor decomposition is used to obtain the combination and difference information between the reference and distorted texture projection maps for mainly characterizing texture distortion of colored PC. Meanwhile, the edge features of the geometry projection map are calculated to measure the global or local geometry distortion. All of the extracted features are combined to predict an overall quality of colored PC. In addition, this paper establishes a multi-distorted colored PC database named CPCD2.0 with compression distortions and Gaussian noise, which orients to the influence of both geometry and texture components in distortion. Experimental results on two open subjective evaluation databases (IRPC and SJTU-PCQA) and the self-built CPCD2.0 database show that the proposed TGP-PCQA method outperforms the state-of-the-art PCQA methods. We are also providing the self-built CPCD2.0 database free of charge at https://github.com/cherry0415/CPCD2.0.