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 degradations of 3D models. However, a large part of previous studies utilize full-reference (FR) metrics, which indicates they can not predict the quality level with 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, machine learning 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 to facilitate further research.

References in corpus (5)

• Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression
• Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training
• Subjective and Objective Visual Quality Assessment of Textured 3D Meshes
• Inferring Point Cloud Quality via Graph Similarity
• Perceptual Quality Assessment of Colored 3D Point Clouds

Cited by in corpus (6)

• Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection
• No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction
• Activating Frequency and ViT for 3D Point Cloud Quality Assessment without Reference
• The Worse The Better: Content-Aware Viewpoint Generation Network for Projection-related Point Cloud Quality Assessment
• No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
• Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features