CPC‐GSCT: Visual quality assessment for coloured point cloud based on geometric segmentation and colour transformation

Abstract

Abstract Coloured point cloud (CPC) is one of the important representations of three‐dimensional objects, which has been used in many fields. CPC may encounter geometric and colour distortion during its compression, simplification or other processing. Thus, the objective visual quality assessment of CPC is one of the urgent issues to be resolved in the CPC's applications. Aiming at this problem, this paper proposes a new full‐reference visual quality assessment metric for CPC based on geometric segmentation and colour transformation (CPC‐GSCT), which analyzes geometric distortion and colour distortion of CPC. First, considering the visual masking effect of CPC's geometric information, CPC is segmented into different regions and distributed with different weights to describe the influence of visual masking effect in CPC quality assessment. At the same time, a geometric combination feature vector is defined and extracted for measuring the CPC's geometric distortion. Then, considering the colour perception of human eyes, a colour combination feature vector is extracted to measure the CPC's colour distortion in HSV colour space. Finally, all the extracted geometric and colour features are constituted as a feature vector to predict the quality of CPC. Experimental results on three databases (IRPC, SJTU‐PCQA and CPCD2.0) show that the proposed CPC‐GSCT metric can achieve better performance in predicting the visual quality of CPC than relevant existing methods.