首页> 外文会议>IEEE International Conference on Image Processing >Cmdm-Vac: Improving A Perceptual Quality Metric For 3D Graphics By Integrating A Visual Attention Complexity Measure
【24h】

Cmdm-Vac: Improving A Perceptual Quality Metric For 3D Graphics By Integrating A Visual Attention Complexity Measure

机译:CMDM-VAC:通过整合视觉关注复杂度测量来改善3D图形的感知质量度量

获取原文

摘要

Many objective quality metrics have been proposed over the years to automate the task of subjective quality assessment. However, few of them are designed for 3D graphical contents with appearance attributes; existing ones are based on geometry and color measures, yet they ignore the visual saliency of the objects. In this paper, we combined an optimal subset of geometry-based and color-based features, provided by a state-of-the-art quality metric for 3D colored meshes, with a visual attention complexity feature adapted to 3D graphics. The performance of our proposed new metric is evaluated on a dataset of 80 meshes with diffuse colors, generated from 5 source models corrupted by commonly used geometry and color distortions. With our proposed metric, we showed that the use of the attentional complexity feature brings a significant gain in performance and better stability.
机译:多年来提出了许多客观质量指标,以自动化主观质量评估的任务。 但是,其中很少有专为3D图形内容而设计的外观属性; 现有的基于几何和颜色测量,但它们忽略了对象的视觉显着性。 在本文中,我们组合了基于几何和基于颜色的特征的最佳子集,由用于3D彩色网格的最先进的质量度量提供,具有视觉关注复杂性特征,适用于3D图形。 我们提出的新度量标准的性能在80个网格的数据集上评估了漫射颜色的数据集,由常用的几何和颜色扭曲损坏的5个源模型生成。 通过我们提出的公制,我们表明,使用注意力复杂性功能带来了显着的性能和更好的稳定性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号