GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute

Date
2023-10-31
Authors
Xing, Jinrui
Yuan, Hui
Hamzaoui, Raouf
Liu, Hao
Hou, Junhui
Journal Title
Journal ISSN
ISSN
Volume Title
Publisher
IEEE
Peer reviewed
Yes
Abstract
In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color components, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on a recent test model of the geometry-based point cloud compression (G-PCC) standard, 0.43 dB, 0.25 dB and 0.36 dB Bjϕntegaard delta (BD)-peak signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3% and 14.5% BD-rate savings were achieved on dense point clouds for the Y, Cb, and Cr components, respectively. The source code of our method is available at https://github.com/xjr998/GQE-Net.
Description
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Keywords
Point clouds, Quality enhancement, Graph neural network, G-PCC
Citation
Xing, J., Yuan, H., Hamzaoui, R., Liu, H. and Hou, J. (2023) GQE-Net: A graph-based quality enhancement network for point cloud color attribute, IEEE Transactions on Image Processing,
Research Institute
Institute of Engineering Sciences (IES)