3 research outputs found
Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review
© 2018 IEEE. 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
No-Reference Quality Assessment for Colored Point Cloud and Mesh Based on Natural Scene Statistics
To improve the viewer's quality of experience and optimize processing systems
in computer graphics applications, the 3D quality assessment (3D-QA) has become
an important task in the multimedia area. Point cloud and mesh are the two most
widely used electronic representation formats of 3D models, the quality of
which is quite sensitive to operations like simplification and compression.
Therefore, many studies concerning point cloud quality assessment (PCQA) and
mesh quality assessment (MQA) have been carried out to measure the visual
quality degradations caused by lossy operations. However, a large part of
previous studies utilizes full-reference (FR) metrics, which means they may
fail to predict the accurate quality level of 3D models when the reference 3D
model is not available. Furthermore, limited numbers of 3D-QA metrics are
carried out to take color features into consideration, which significantly
restricts the effectiveness and scope of application. In many quality
assessment studies, natural scene statistics (NSS) have shown a good ability to
quantify the distortion of natural scenes to statistical parameters. Therefore,
we propose an NSS-based no-reference quality assessment metric for colored 3D
models. In this paper, quality-aware features are extracted from the aspects of
color and geometry directly from the 3D models. Then the statistic parameters
are estimated using different distribution models to describe the
characteristic of the 3D models. Our method is mainly validated on the colored
point cloud quality assessment database (SJTU-PCQA) and the colored mesh
quality assessment database (CMDM). The experimental results show that the
proposed method outperforms all the state-of-art NR 3D-QA metrics and obtains
an acceptable gap with the state-of-art FR 3D-QA metrics
A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network
International audienceNo-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully assesses the visual quality, in addition, the experimental results show that the proposed method provides good correlations with the subject scores and competitive scores comparing to some influential and effective full and reduced reference existing metrics