In this paper, we present a full-reference quality assessment metric based on the information of visual saliency. The saliency information is provided under the form of degrees associated to each vertex of the surface mesh. From these degrees, statistical attributes reflecting the structures of the reference and distorted meshes are computed. These are used by four comparisons functions genetically optimized that quantify the structure differences between a reference and a distorted mesh. We also present a statistical comparison study of six full-reference quality assessment metrics for 3D meshes. We compare the objective metrics results with humans subjective scores of quality considering the 3D meshes in one hand and the distorsion types in the other hand. Also, we show which metrics are statistically superior to their counterparts. For these comparisons we use the Spearman Rank Ordered Correlation Coefficient and the hypothetic test of Student (ttest). To attest the pertinence of the proposed approach, a comparison with a ground truth saliency and an application associated to the assessment of the visual rendering of smoothing algorithms are presented. Experimental results show that the proposed metric is very competitive with the state-of-the-art.

中文翻译：

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用于 FR 3D 网格质量评估的视觉显着性和粗糙度的遗传组合：统计研究

在本文中，我们提出了一种基于视觉显着性信息的全参考质量评估指标。显着性信息以与表面网格的每个顶点相关联的度数的形式提供。根据这些程度，可以计算反映参考和扭曲网格结构的统计属性。这些由四个基因优化的比较函数使用，用于量化参考和扭曲网格之间的结构差异。我们还对 3D 网格的六个全参考质量评估指标进行了统计比较研究。我们一方面考虑 3D 网格，另一方面考虑失真类型，将客观指标结果与人类主观质量分数进行比较。此外，我们还展示了哪些指标在统计​​上优于其对应指标。对于这些比较，我们使用 Spearman Rank Ordered Correlation Coefficient 和 Student 的假设检验 (ttest)。为了证明所提出的方法的相关性，提出了与地面实况显着性的比较以及与平滑算法的视觉渲染评估相关的应用程序。实验结果表明，所提出的指标与最先进的指标非常具有竞争力。