利用显著性的点云模型客观质量评价

doi:10.3969/j.issn.0255-8297.2014.05.001

应用科学学报 ›› 2014, Vol. 32 ›› Issue (5): 441-446.doi: 10.3969/j.issn.0255-8297.2014.05.001

• RESEARCHNOTES • 上一篇下一篇

利用显著性的点云模型客观质量评价

张娟1,2，张习民1,2，万旺根1,2，方志军1,2

1. 上海大学通信与信息工程学院，上海200444
2. 上海大学智慧城市研究院，上海200444

收稿日期:2013-12-12 修回日期:2014-01-16 出版日期:2014-09-23 发布日期:2014-01-16
作者简介:张娟，博士，研究方向：多媒体技术，E-mail：zhang-j@foxmail.com；万旺根，教授，博导，研究方向：计算机图形学与虚拟现实技术，E-mail：wanwg@staff.shu.edu.cn
基金资助:
国家自然科学基金(No.61373084)；国家“863”高技术研究发展计划基金(No.2013AA01A603)；上海市教育委员会科研创新项目基金(No.14YZ011)资助

Saliency Based Quality Evaluation of Point Cloud Model

ZHANG Juan1,2, ZHANG Xi-min1,2, WAN Wang-gen1,2, FANG Zhi-jun1,2

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
2. Institute of Smart City, Shanghai University, Shanghai 200444, China

Received:2013-12-12 Revised:2014-01-16 Online:2014-09-23 Published:2014-01-16

摘要/Abstract

摘要： 点云模型质量的客观评价是三维几何模型研究中不可或缺的工作. 根据显著性能够较好地反映人类视觉系统的特点，提出以点的法向量与其邻域点法向量的差异作为该点的显著性计算依据，利用原始点云模型和处理后点云模型在显著性上的差异判断点云模型质量的变化，实现点云模型的质量评价. 实验结果表明，所提出的客观评价模型和主观的人工评价得出的结果基本一致，且比信噪比方法更能体现人的视觉感知.

关键词: 点云模型, 显著性, 法向量, 质量评价

Abstract: To deal with the huge amount of data in point cloud models, data must be compressed before use.Effective evaluation of quality degradation of the compressed point cloud model with respect to the original is needed. This work defines saliency of a cloud point that well reflects the human visual characteristics as the angular difference between the normal vector at the point and the average of normal vectors in its neighborhood. The difference in saliency between the compressed point cloud model and the original is taken as the objective quality index, which is easy to be calculated. Experiments on point cloud models with various resolutions indicate that the proposed method can give results conform to subjective evaluation.

Key words: point cloud model, saliency, normal vector, quality evaluation

中图分类号:

TP311

张娟1,2，张习民1,2，万旺根1,2，方志军1,2. 利用显著性的点云模型客观质量评价[J]. 应用科学学报, 2014, 32(5): 441-446.

ZHANG Juan1,2, ZHANG Xi-min1,2, WAN Wang-gen1,2, FANG Zhi-jun1,2. Saliency Based Quality Evaluation of Point Cloud Model[J]. Journal of Applied Sciences, 2014, 32(5): 441-446.

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利用显著性的点云模型客观质量评价

Saliency Based Quality Evaluation of Point Cloud Model

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