A Reduced-Reference Quality Assessment Metric for Textured Mesh Digital Humans
Zicheng Zhang, Yingjie Zhou, Chunyi Li, Kang Fu, Wei Sun, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai
Abstract
In an era where 3D Digital Humans (DHs) are becoming increasingly prevalent in fields like gaming, automotive, and the metaverse, the demand for high DH visual quality is rising. This paper presents the first-ever reduced-reference (RR) quality assessment metric tailored specifically for textured mesh DHs, aiming to optimize transmission systems and improve Quality of Experience (QoE) for viewers in resource-constrained environments. Four critical geometric curvature-related attributes and two texture-related indicators are computed, which are then statistically analyzed and utilized in a Support Vector Regression (SVR) model for robust and efficient quality prediction. Experimental results confirm that our method outperforms existing full-reference (FR) metrics, making it an invaluable tool for the future of 3D DHs in various applications. The code is available at https://github.com/zzc-1998/RR-DHQA.