Perceptual Quality Assessment for Digital Human Heads
Zicheng Zhang, Yingjie Zhou, Wei Sun, Xiongkuo Min, Yuzhe Wu, Guangtao, Zhai

TL;DR
This paper introduces a large-scale database and a novel projection-based method for assessing the perceptual quality of 3D scanned digital human heads, achieving state-of-the-art performance.
Contribution
It provides the first comprehensive database and a new effective full-reference quality assessment method for digital human heads.
Findings
The proposed method outperforms existing metrics in quality prediction.
The database enables standardized evaluation of digital human head quality.
State-of-the-art results demonstrate the effectiveness of the approach.
Abstract
Digital humans are attracting more and more research interest during the last decade, the generation, representation, rendering, and animation of which have been put into large amounts of effort. However, the quality assessment of digital humans has fallen behind. Therefore, to tackle the challenge of digital human quality assessment issues, we propose the first large-scale quality assessment database for three-dimensional (3D) scanned digital human heads (DHHs). The constructed database consists of 55 reference DHHs and 1,540 distorted DHHs along with the subjective perceptual ratings. Then, a simple yet effective full-reference (FR) projection-based method is proposed to evaluate the visual quality of DHHs. The pretrained Swin Transformer tiny is employed for hierarchical feature extraction and the multi-head attention module is utilized for feature fusion. The experimental results…
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Taxonomy
TopicsImage and Video Quality Assessment · Infrared Thermography in Medicine · Visual Attention and Saliency Detection
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Stochastic Depth · Position-Wise Feed-Forward Layer · Byte Pair Encoding · Adam · Softmax · Dropout · Residual Connection
