Paper Info
Title
No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models
Abstract
To improve the viewer’s Quality of Experience (QoE) and optimize computer graphics applications, 3D model quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most widely used digital representation formats of 3D models, the visual quality of which is quite sensitive to lossy operations like simplification and compression. Therefore, many related studies such as point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality of distorted 3D models. However, most previous studies utilize full-reference (FR) metrics, which indicates they can not predict the quality level in the absence of the reference 3D model. Furthermore, few 3D-QA metrics consider color information, which significantly restricts their effectiveness and scope of application. In this paper, we propose a no-reference (NR) quality assessment metric for colored 3D models represented by both point cloud and mesh. First, we project the 3D models from 3D space into quality-related geometry and color feature domains. Then, the 3D natural scene statistics (3D-NSS) and entropy are utilized to extract quality-aware features. Finally, a support vector regression (SVR) model is employed to regress the quality-aware features into visual quality scores. Our method is validated on the colored point cloud quality assessment database (SJTU-PCQA), the Waterloo point cloud assessment database (WPC), and the colored mesh quality assessment database (CMDM). The experimental results show that the proposed method outperforms most compared NR 3D-QA metrics with competitive computational resources and greatly reduces the performance gap with the state-of-the-art FR 3D-QA metrics. The code of the proposed model is publicly available now at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zzc-1998/NR-3DQA</uri> .
Year
DOI
Venue
2022
10.1109/TCSVT.2022.3186894
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
3D model quality assessment,colored point cloud,colored mesh,no-reference quality assessment,natural scene statistics
Journal
32
Issue
ISSN
Citations 
11
1051-8215
0
PageRank 
References 
Authors
0.34
33
6
Name
Order
Citations
PageRank
Zicheng Zhang111.70
Wei Sun246928.26
Xiongkuo Min333740.88
Wang Tao41420.89
Wei Lu553449.25
Guangtao Zhai61707145.33