Impact of Point Rendering Methods on the Subjective Quality of Quantization Point Clouds and Algorithm Optimization

CHEN Zhang; YIN Yujie; FENG Yun; WAN Shuai

doi:10.7652/xjtuxb202404003

您当前的位置：

首页 >

文章列表页 >

Impact of Point Rendering Methods on the Subjective Quality of Quantization Point Clouds and Algorithm Optimization

更新时间：2025-07-09

- Impact of Point Rendering Methods on the Subjective Quality of Quantization Point Clouds and Algorithm Optimization
- Vol. 58, Issue 4, Pages: 32-40(2024)
- 作者机构：
  
  1. 西北工业大学电子信息学院,西安,710129
  2. 河海大学信息学部计算机与信息学院,南京,210024
  3. 皇家墨尔本理工大学工程学院, VIC,墨尔本,澳大利亚,3001
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202404003
  CLC： TP391.4
- Online First：10 April 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
CHEN Zhang, YIN Yujie, FENG Yun, et al. Impact of Point Rendering Methods on the Subjective Quality of Quantization Point Clouds and Algorithm Optimization[J]. 2024, 58(4): 32-40.
DOI：

CHEN Zhang, YIN Yujie, FENG Yun, et al. Impact of Point Rendering Methods on the Subjective Quality of Quantization Point Clouds and Algorithm Optimization[J]. 2024, 58(4): 32-40. DOI： 10.7652/xjtuxb202404003.

摘要

为探究点渲染方式对量化点云主观质量的影响并优化渲染算法

设计了不同基础几何体类型和渲染半径计算方式的点云主观质量评价实验。通过威尔科克森符号秩检验和双独立样本T检验

分析了基础几何体类型和最近邻渲染算法对几何和属性量化失真点云主观质量的影响。实验结果表明:基础几何体类型由于混叠程度不同而对主观质量影响不同

基础几何体混叠面积越大则主观质量越低; 最近邻渲染算法对主观质量影响不显著

该算法减少了点云渲染后空洞的产生

却增大了混叠程度。在属性失真较小时

最近邻渲染算法效果优于渲染半径固定算法; 在属性失真较大时

则劣于渲染半径固定的算法。结合主观实验结果

建立了混叠、空洞失真和基础几何体渲染半径的数学模型

在几何量化采用八叉树剪枝的基础上

通过空间相似性对模型参数进行简化并求解

提出一种利用几何量化参数计算基础几何体渲染半径的算法。与最近邻渲染算法相比

所提算法节省了52%的时间复杂度

渲染点云峰值信噪比提升了12.3%

主观质量分数提升了0.5。研究减少了计算资源

提高了渲染效率

可为渲染器的设计和优化提供参考。

Abstract

To investigate the impact of point rendering methods on the subjective quality of quantization point clouds and optimize the rendering algorithm

a subjective quality evaluation experiment is designed

considering different basic geometric types and calculation methods for rendering radius. The effects of the basic geometric types and nearest-neighbor rendering algorithm on the subjective quality of geometric and attribute quantization distortion are analyzed through the Wilcoxon signed-rank test and independent samples T-test. The experimental results show that the subjective quality is affected differently by the basic geometric types due to variations in the degree of overlap. A larger overlapping area of the basic geometric types shall result in lower subjective quality. The nearest-neighbor rendering algorithm does not have any significant impact on the subjective quality. This algorithm reduces the occurrence of holes in the rendered point cloud but increases the level of overlap. When the attribute distortion is minor

the nearest-neighbor rendering algorithm outperforms the fixed rendering radius method. However

when the attribute distortion is significant

it underperforms the fixed rendering radius method. Based on the subjective experimental results

a mathematical model is established to calculate the rendering radius of the basic geometric types

considering overlap and the distortion of holes. This model has employed geometric quantization with an octree pruning and simplified the model parameters using spatial similarity. An algorithm is proposed to calculate the rendering radius of the basic geometric types using geometric quantization parameters. Compared with the nearest-neighbor rendering algorithm

the proposed method reduces the time complexity by 52% and improves the peak signal-to-noise ratio of the rendered point cloud by 12.3%. Additionally

it enhances the subjective quality by 0.5 points. The research

featuring the reduction of computational resources and the improvement of rendering efficiency

can provide references for the design and optimization of renderers.

关键词

Keywords

references

李厚强, 李礼, 李竹. 点云编码综述 [J]. 中兴通讯技术, 2021, 27(1): 4-9.

LI Houqiang, LI Li, LI Zhu. A review of point cloud compression [J]. ZTE Technology Journal, 2021, 27(1): 4-9.

FAN Y, ZHANG Z, SUN W, et al. A no-reference quality assessment metric for point cloud based on captured video sequences [C]//IEEE 24th International Workshop on Multimedia Signal Processing. Piscataway, NJ, USA: IEEE, 2022: 1-5.

LI Zhu, LIU Shan, DUFAUX F, et al. Guest editorial introduction to the special issue on recent advances in point cloud processing and compression [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4555-4560.

徐异凌, 杨琦, 杨开发, 等. 点云质量评价挑战与关键技术研究 [J]. 中国传媒大学学报(自然科学版), 2021, 28(5): 11-21.

XU Yiling, YANG Qi, YANG Kaifa, et al. Challenges and key technologies of point cloud quality assessment [J]. Journal of Communication University of China Science and Technology, 2021, 28(5): 11-21.

KIVI P, MAKITALO M, ZADNIL J, et al. Real-time rendering of point clouds with photorealistic effects: a survey [J]. IEEE Access, 2022, 10: 13151-13173.

KOPANAS G, PHILIP J, LEIMKÜHLER T, et al. Point-based neural rendering with per-view optimization [J]. Computer Graphics Forum, 2021, 40(4): 29-43.

BUI G, LE T, MORAGO B, et al. Point-based rendering enhancement via deep learning [J]. The Visual Computer, 2018, 34(6): 829-841.

ALEXIOUS E, UPENIK E, EBRAHIMI T. Towards subjective quality assessment of point cloud imaging in augmented reality [C]//IEEE 19th International Workshop on Multimedia Signal Processing. Piscataway, NJ, USA: IEEE, 2017: 1-6.

ALEXIOUS E, EBRAHIMI T. On the performance of metrics to predict quality in point cloud representations [C]// Proceedings of SPIE. Bellingham, WA, USA: SPIE, 2017: 10396.

ALEXIOU E, EBRAHIMI T. Impact of visualisation strategy for subjective quality assessment of point clouds [C]//2018 IEEE International Conference on Multimedia Expo Workshops(ICMEW). Piscataway, NJ, USA: IEEE, 2018: 1-6.

ALEXIOU E, EBRAHIMI T, BERNARDO M V, et al. Point cloud subjective evaluation methodology based on 2D rendering [C]//2018 Tenth International Conference on Quality of Multimedia Experience(QoMEX). Piscataway, NJ, USA: IEEE, 2018: 1-6.

JAVAHERI A, BRITES C, PEREIRA F, et al. Point cloud rendering after coding: impacts on subjective and objective quality [J]. IEEE Transactions on Multimedia, 2020, 23: 4049-4064.

YANG Qi, CHEN Hao, MA Zhan, et al. Predicting the perceptual quality of point cloud: a 3D-to-2D projection-based exploration [J]. IEEE Transactions on Multimedia, 2021, 23: 3877-3891.

YANG Qi. Shanghai Jiao Tong university subjective point cloud quality assessment(SJTU-PCQA)database [DB/OL]. [2023-05-20] https: //smt.sjtu.edu.cn/database/point-cloud-subjective-assessment-database.

WU Xinju, ZHANG Yun, FAN Chunling, et al. Subjective quality database and objective study of compressed point clouds with 6DoF head-mounted display [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4630-4644.

WU X, ZHANG Y, FAN C, et al. SIAT-PCQD: subjective point cloud quality database with 6d of head-mounted display [EB/OL]. [2023-05-01] https: //ieee-dataport.org/documents/siat-pcqd-subjective-point-cloud-quality-database-6dof-head-mounted-display.

LIU Qi, SU Honglei, DUANMU Zhengfang, et al. Perceptual quality assessment of colored 3D point clouds [J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(8): 3642-3655.

MPEG-3DG. MPEG static object and scenes datasets [DB/OL]. [2023-05-20]. http: //mpegfs.intevry.fr/MPEG/PCC/DataSets/pointCloud/CfP/datasets/Static_Objects_and_Scenes/.

SCHWARZ S, PREDA M, BARONCINI V, et al. Emerging MPEG standards for point cloud compression [J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019, 9(1): 133-148.

MPEG-3DG. Common test conditions for G-PCC [EB/OL].(2023-05-01)[2023-06-04]. https: //dms.mpeg.expert/doc_end_user/.

ITU. Subjective video quality assessment methods for multimedia applications: ITU-T P.9210 [R]. Geneva, Switzerland: ITU, 2016.

ITU. Methodology for the subjective assessment of the quality of television pictures: ITU-R BT.500 [R]. Geneva, Switzerland: ITU, 2012: 500-513.

JANOWSKI L, PINSON M. The accuracy of subjects in a quality experiment: a theoretical subject model [J]. IEEE Transactions on Multimedia, 2015, 17(12): 2210-2224.

GOLENKO D, AMPILOV O. Unknown signal detection based on Wilcoxon signed-rank test [C]// Proceedings of 2016 International Conference on Engineering and Telecommunication. Piscataway, NJ, USA: IEEE, 2016: 72-75.

吴鑫菊. 面向六自由度虚拟现实的点云视觉质量评价方法 [D]. 广东深圳: 中国科学院深圳先进技术研究院, 2021.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Sensitivity Analysis of Foil Geometrical Parameters for the Third-Generation CO2 Gas Foil Bearings Based on Design of Experiments Method

Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration

Integrated and Joint Design Optimization of Centrifugal Compressor Impeller and Casing Treatment at Multiple Working Points

Effects of Expansion Angle Crater Film Hole on the Film Cooling Characteristics of a Double-Wall Structure

A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction

Related Author

No data

Related Institution

No data

⁰