Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review

Abstract

During mesh processing operations (e.g. simplifications, compression, and watermarking), a 3D triangle mesh is subject to various visible distortions on mesh surface which result in a need to estimate visual quality. The necessity of perceptual quality evaluation is already established, as in most cases, human beings are the end users of 3D meshes. To measure such kinds of distortions, the metrics that consider geometric measures integrating human visual system (HVS) is called perceptual quality metrics. In this paper, we direct an expansive study on 3D mesh quality evaluation mostly focusing on recently proposed perceptual based metrics. We limit our study on greyscale static mesh evaluation and attempt to figure out the most workable method for real-time evaluation by making a quantitative comparison. This paper also discusses in detail how to evaluate objective metric's performance with existing subjective databases. In this work, we likewise research the utilization of the psychometric function to expel non-linearity between subjective and objective values. Finally, we draw a comparison among some selected quality metrics and it shows that curvature tensor based quality metrics predicts consistent result in terms of correlation.

Automated Visual Quality Assessment for Virtual and Augmented Reality based Digital Twins

“…Such metrics commonly include measures of curvature ( [34], [38], [40], [43] and [44]), dihedral angle ( [28] and [33]), normal vectors (see [45]) and surface roughness (see [34] and [39]). These metrics are seen to have a stronger correlation to visual perception than simpler measures such as the Hausdorff distance ( [26] and [38]).…”

“…Model based metrics are based directly on observable geometric features of the mesh itself. Commonly used features include simple measurements such as mesh volume and surface area, as well more complex features such as curvature and dihedral angles ( [22], [26], [28], [32], [33] and [34]). Model-based methods are further classified as full-reference, part-reference, or no-reference (blind) according to the availability of the reference mesh.…”

“…Part-reference methods do not have access to the reference mesh but do have access to some data regarding it. No-reference models only have access to the test mesh, [26]. Full and part-reference methods tend to work by comparing the values of one or more geometric parameters between the reference and test meshes.…”

“…In all cases, the principal aim of VQA is to determine the impact on the visual quality of a 3D mesh caused by one or more modification processes, [23]. In many cases, VQA methods are applied to predict if a given process is likely to lead to unacceptable distortion in the appearance of particular mesh, [26]. The likelihood of this distortion is seen to depend heavily on both the geometry of the mesh and the modification process used.…”

Optimised Models for Ar/Vr by Using Geometric Complexity Metrics to Control Tessellation

“…MSDM2 (Lavoué, 2011) use these estimators separately. Machine learning based approaches often combine different estimators to improve the correlation to the MOS (Abouelaziz et al, 2015;Feng et al, 2018;M Muzahid et al, 2018;Yildiz et al, 2020).…”