Blind quality assessment of dense 3D point clouds with structure guided resampling

Zhou, Wei, Yang, Qi, Chen, Wu, Jiang, Qiuping, Zhai, Guangtao and Lin, Weisi 2024. Blind quality assessment of dense 3D point clouds with structure guided resampling. ACM Transactions on Multimedia Computing, Communications and Applications 20 (8) 10.1145/3664199

Preview

PDF - Accepted Post-Print Version Download (1MB) | Preview

Abstract

Objective quality assessment of 3D point clouds is essential for the development of immersive multimedia systems in real-world applications. Despite the success of perceptual quality evaluation for 2D images and videos, blind/no-reference metrics are still scarce for 3D point clouds with large-scale irregularly distributed 3D points. Therefore, in this paper, we propose an objective point cloud quality index with Structure Guided Resampling (SGR) to automatically evaluate the perceptually visual quality of dense 3D point clouds. The proposed SGR is a general-purpose blind quality assessment method without the assistance of any reference information. Specifically, considering that the human visual system (HVS) is highly sensitive to structure information, we first exploit the unique normal vectors of point clouds to execute regional pre-processing which consists of keypoint resampling and local region construction. Then, we extract three groups of quality-related features, including: 1) geometry density features; 2) color naturalness features; 3) angular consistency features. Both the cognitive peculiarities of the human brain and naturalness regularity are involved in the designed quality-aware features that can capture the most vital aspects of distorted 3D point clouds. Extensive experiments on several publicly available subjective point cloud quality databases validate that our proposed SGR can compete with state-of-the-art full-reference, reduced-reference, and no-reference quality assessment algorithms.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery (ACM)
ISSN:	1551-6857
Date of First Compliant Deposit:	11 May 2024
Date of Acceptance:	4 May 2024
Last Modified:	09 Nov 2024 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/168856

Actions (repository staff only)

Edit Item