Fast Coding Unit Partitioning Method for Video-Based Point Cloud Compression: Combining Convolutional Neural Networks and Bayesian Optimization

As 5G technology and 3D capture techniques have been rapidly developing, there has been a remarkable increase in the demand for effectively compressing dynamic 3D point cloud data. Video-based point cloud compression (V-PCC), which is an innovative method for 3D point cloud compression, makes use of...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 14; no. 7; p. 1295
Main Authors: Song, Wenjun, Liu, Xinqi, Zhang, Qiuwen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2025
Subjects:
Algorithms
Analysis
Artificial neural networks
Bayesian analysis
Classification
Complexity
Computing time
Credit unions
Cultural heritage
Deep learning
Efficiency
Geometry
Image coding
Machine learning
Methods
Neural networks
Optimization
Partitioning
Software
Three dimensional models
Video compression
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:As 5G technology and 3D capture techniques have been rapidly developing, there has been a remarkable increase in the demand for effectively compressing dynamic 3D point cloud data. Video-based point cloud compression (V-PCC), which is an innovative method for 3D point cloud compression, makes use of High-Efficiency Video Coding (HEVC) to carry out the compression of 3D point clouds. This is accomplished through the projection of the point clouds onto two-dimensional video frames. However, V-PCC faces significant coding complexity, particularly for dynamic 3D point clouds, which can be up to four times more complex to process than a conventional video. To address this challenge, we propose an adaptive coding unit (CU) partitioning method that integrates occupancy graphs, convolutional neural networks (CNNs), and Bayesian optimization. In this approach, the coding units (CUs) are first divided into dense regions, sparse regions, and complex composite regions by calculating the occupancy rate R of the CUs, and then an initial classification decision is made using a convolutional neural network (CNN) framework. For regions where the CNN outputs low-confidence classifications, Bayesian optimization is employed to refine the partitioning and enhance accuracy. The findings from the experiments show that the suggested method can efficiently decrease the coding complexity of V-PCC, all the while maintaining a high level of coding quality. Specifically, the average coding time of the geometric graph is reduced by 57.37%, the attribute graph by 54.43%, and the overall coding time by 54.75%. Although the BD rate slightly increases compared with that of the baseline V-PCC method, the impact on video quality is negligible. Additionally, the proposed algorithm outperforms existing methods in terms of geometric compression efficiency and computational time savings. This study’s innovation lies in combining deep learning with Bayesian optimization to deliver an efficient CU partitioning strategy for V-PCC, improving coding speed and reducing computational resource consumption, thereby advancing the practical application of V-PCC.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14071295