Perceptual Quality Assessment for 3D Triangle Mesh Based on Curvature

Dong, Lü; Fang, Yuming; Wang, Lin; Seah, Hock Soon

doi:10.1109/tmm.2015.2484221

Cited by 30 publications

(26 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both of these metrics are independent of connectivity and designed for static meshes. Dong et al [16] propose a novel roughness-based perceptual quality assessment method. The novelty of the metric lies in the incorporation of structural similarity, visual masking, and saturation effect which are highly employed in quality assessment methods separately.…”

Section: Model-based Perceptual Metricsmentioning

confidence: 99%

A perceptual quality metric for dynamic triangle meshes

Yıldız

Çapın

2017

J Image Video Proc.

View full text Add to dashboard Cite

A measure for assessing the quality of a 3D mesh is necessary in order to determine whether an operation on the mesh, such as watermarking or compression, affects the perceived quality. The studies on this field are limited when compared to the studies for 2D. In this work, we aim a full-reference perceptual quality metric for animated meshes to predict the visibility of local distortions on the mesh surface. The proposed visual quality metric is independent of connectivity and material attributes. Thus, it is not associated to a specific application and can be used for evaluating the effect of an arbitrary mesh processing method. We use a bottom-up approach incorporating both the spatial and temporal sensitivity of the human visual system. In this approach, the mesh sequences go through a pipeline which models the contrast sensitivity and channel decomposition mechanisms of the HVS. As the output of the method, a 3D probability map representing the visibility of distortions is generated. We have validated our method by a formal user experiment and obtained a promising correlation between the user responses and the proposed metric. Finally, we provide a dataset consisting of subjective user evaluation of the quality of public animation datasets.

show abstract

Section: Model-based Perceptual Metricsmentioning

confidence: 99%

A perceptual quality metric for dynamic triangle meshes

Yıldız

Çapın

2017

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Here the local roughness is measured by Laplacian of the discrete Gaussian curvature. Another curvature based roughness estimation parameter is proposed by [6] where Gaussian [19] on which they computed the local statistics to generate a perceptual similarity index SMQI (Saliency based Mesh Quality Index). These perceptual quality based metrics are correlated with the subjective assessment of the same meshes.…”

Section: Curvature Based Measuresmentioning

confidence: 99%

Evaluation Metrics used in 3D Mesh Watermarking: A Study

Borah¹,

Borah²

2017

IJSIA

View full text Add to dashboard Cite

show abstract

“…Most existing Mesh Visual Quality (MVQ) metrics construct a local quality map (distortion map) that provides vertex-wise or edge-wise quality (distortion), such as MSDM [1], MSDM2 [2], DAME [3], FMPD [4], TPDM [5], Lu [6], and then pool the local quantities into a final score that reflects the overall quality of the distorted mesh. The commonly used pooling strategies are average weighting [3]- [4], [6] and weighted Minkowski sum [1]- [2], [5].…”

Section: Introductionmentioning

confidence: 99%

“…The commonly used pooling strategies are average weighting [3]- [4], [6] and weighted Minkowski sum [1]- [2], [5]. However, the pooling strategy of objective scores in mesh quality assessment is less investigated.…”

Section: Introductionmentioning

confidence: 99%

“…The studies in [1] and [7] at some degree illustrated the similarity of HVS in image quality assessment and mesh quality assessment. The image quality assessment community has conducted significant studies on using machine learning techniques to support IVQ assessment [11]- [17], while most existing mesh quality assessment approaches [1]- [6] attempt to model the HVS explicitly. Lavoue et al [18] proposed a perceptual evaluation metric based on multiple attributes using machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A perceptual quality metric for 3D triangle meshes based on spatial pooling

Feng

Wan

et al. 2018

Front. Comput. Sci.

View full text Add to dashboard Cite

In computer graphics, various processing operations are applied on 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In such context, perceptual quality assessment of 3D triangle meshes becomes a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of distortion distribution. We generate a perceptual distortion map for vertices in reference mesh while taking into account the visual masking effect of human visual system. The proposed metric extracts statistical descriptors from distortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt Support Vector Regression model to predict the mesh quality score. We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores.

show abstract

Perceptual Quality Assessment for 3D Triangle Mesh Based on Curvature

Cited by 30 publications

References 26 publications

A perceptual quality metric for dynamic triangle meshes

A perceptual quality metric for dynamic triangle meshes

Evaluation Metrics used in 3D Mesh Watermarking: A Study

A perceptual quality metric for 3D triangle meshes based on spatial pooling

Contact Info

Product

Resources

About