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对于压缩点云的基于支持向量的回归的减少参考感知质量模型.

Honglei Su1, Qi Liu2, Hui Yuan3

  • 1College of Electronics and Information, Qingdao University, Qingdao 266071, China.

IEEE transactions on multimedia
|November 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了PCQAML,这是一种基于视频的点云压缩 (V-PCC) 的新型减少参考质量指标. PCQAML有效地使用选择的特征预测感知质量,在准确性和效率方面超过现有指标.

关键词:
拉索回归法 (Lasso Regression) 是一种回归法.点云压缩点云的压缩功能选择 功能选择感知质量指标是感知质量指标.支持向量的回归.

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科学领域:

  • 计算机视觉 计算机视觉
  • 信号处理 信号处理
  • 多媒体压缩压缩.

背景情况:

  • 基于视频的点云压缩 (V-PCC) 是用于压缩3D数据的MPEG标准.
  • 现有的质量指标通常需要原始点云,限制实时应用程序.
  • 当原始数据不可用时,需要减少引用 (RR) 度量.

研究的目的:

  • 为V-PCC.开发一种新的减少参考质量指标.
  • 为解决扭曲点云的特征选择和感知质量映射方面的挑战.
  • 为V-PCC.提供准确有效的质量评估工具.

主要方法:

  • 提出了一个包括压缩,几何,正常,曲率和亮度在内的综合功能集.
  • 使用最小绝对收缩和选择运算符 (LASSO) 进行有效的特征选择.
  • 将选定的特征映射到非线性空间中的平均意见得分 (MOS).

主要成果:

  • 拟议的指标PCQAML在基准数据集 (WPC2.0,M-PCCD) 上表现出卓越的表现.
  • PCQAML的性能超过了最先进的全参考和缩小参考指标.
  • 实现了高相关系数 (皮尔森,斯皮尔曼,肯德尔) 和低RMSE,表明了强大的预测准确性.

结论:

  • 在V-PCC中,PCQAML提供了一种灵活而准确的解决方案,用于V-PCC中的简化参考质量评估.
  • 该方法有效地使用选定的特征来表征视觉质量.
  • 对于需要感知质量评估的实时V-PCC应用程序,PCQAML是一个有前途的工具.