Visual Quality of 3D Meshes With Diffuse Colors in Virtual Reality: Subjective and Objective Evaluation

Abstract

Surface meshes associated with diffuse texture or color attributes are becoming popular multimedia contents. They provide a high degree of realism and allow six degrees of freedom (6DoF) interactions in immersive virtual reality environments. Just like other types of multimedia, 3D meshes are subject to a wide range of processing, e.g., simplification and compression, which result in a loss of quality of the final rendered scene. Thus, both subjective studies and objective metrics are needed to understand and predict this visual loss. In this work, we introduce a large dataset of 480 animated meshes with diffuse color information, and associated with perceived quality judgments. The stimuli were generated from 5 source models subjected to geometry and color distortions. Each stimulus was associated with 6 hypothetical rendering trajectories (HRTs): combinations of 3 viewpoints and 2 animations. A total of 11520 quality judgments (24 per stimulus) were acquired in a subjective experiment conducted in virtual reality. The results allowed us to explore the influence of source models, animations and viewpoints on both the quality scores and their confidence intervals. Based on these findings, we propose the first metric for quality assessment of 3D meshes with diffuse colors, which works entirely on the mesh domain. This metric incorporates perceptually-relevant curvature-based and color-based features. We evaluate its performance, as well as a number of Image Quality Metrics (IQMs), on two datasets: ours and a dataset of distorted textured meshes. Our metric demonstrates good results and a better stability than IQMs. Finally, we investigated how the knowledge of the viewpoint (i.e., the visible parts of the 3D model) may improve the results of objective metrics.

A No-Reference Visual Quality Metric For 3D Color Meshes

“…The method proposed in this paper is validated on the color mesh distortion measure (CMDM) database [11]. The database is generated from 5 source models subjected to geometry and color distortions.…”

“…Then the features are collected and integrated into quality scores using the support vector regression (SVR) model. To demonstrate the accuracy and stability of the proposed metric on the color mesh distortion measure (CMDM) database [11], several representative IQA metrics along with model-based metrics are selected as competitors. Experimental results show that the proposed metric achieves the highest correlation with the subjective scores.…”

“…Fig.3. Example of a reference 3D mesh and its corresponding geometry-distorted mesh from the CMDM database[11]. (a) and (b) are snapshots of the reference and its corresponding geometry-distorted mesh, (c) and (d) are the curvature maps of the reference and its corresponding geometry-distorted mesh, (e) and (f) are the normalized curvature probability distributions of the meshes respectively.…”

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

“…The metric we propose is an extension of the Color Mesh Distortion Measure (CMDM) [4], which is a full-reference metric that operates entirely on the mesh domain and incorporates accurate geometry and color features. The novelty of our metric is the integration of the Visual Attention Complexity (VAC) indicator [9] to these latter features.…”

“…With respect to 3D content with color or material information, very few works have been published: metrics proposed for meshes with diffuse texture [2,3] are usually based on a combination of mesh quality and texture quality. Very recently, the Color Mesh Distortion Measure (CMDM) [4] was proposed for 3D meshes with diffuse colors. It is the first metric that operates entirely on the mesh domain.…”