A Curvature Tensor Distance for Mesh Visual Quality Assessment

Torkhani, Fakhri; Wang, Kai; Chassery, Jean‐Marc

doi:10.1007/978-3-642-33564-8_31

Cited by 38 publications

(29 citation statements)

References 19 publications

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“…A visual similarity metric Tensor based Perceptual Distortion Measure (TPDM) is introduced by Torkhani et al [21] which is based on the measurement of a distance between curvature tensors of the two triangle meshes under comparison. To Compute TPDM at first the Local Curvature Tensors (LTD) is calculated by finding the curvature tensors for every vertex of the mesh.…”

Section: Curvature Based Measuresmentioning

confidence: 99%

Evaluation Metrics used in 3D Mesh Watermarking: A Study

Borah¹,

Borah²

2017

IJSIA

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Section: Curvature Based Measuresmentioning

confidence: 99%

Evaluation Metrics used in 3D Mesh Watermarking: A Study

Borah¹,

Borah²

2017

IJSIA

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“…Wang et al [32] proposed a fast mesh perceptual distance (FMPD) metric which considers the visual masking effect and psychometric saturation effect of the HVS. More recently, TPDM [33], DAME [34], [35] were proposed which also serve as reliable quality indicators for mesh models.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Quality and Visual Experience Analysis for Polygon Mesh on Different Display Devices

Yang

Liu

et al. 2018

IEEE Access

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Polygon mesh models have been widely used in various areas due to its high degree of verisimilitude and interactivity. Since the mesh models usually undergo various phases of signal processing for the purpose of storage, simplification, transmission, and deformation, the perceptual quality as well as the visual experience of mesh models are often subject to distortions at every stage. Therefore, investigating the perceptual quality and the visual experience of mesh models have become one of the major tasks for both the academia and industry. In this paper, we have designed two subjective experiments to investigate the perceptual quality and the visual experience in both the virtual reality environment and the traditional 2-D environment. Experimental results showed that there is no statistically significant difference in the quality perception between the two viewing conditions, independent of the model content, the distortion type, and the distortion level. On the contrary, there exists significant difference in the visual experience between the two viewing conditions under various factors. This paper helps researchers to better understand the quality perception behavior and the visual experience toward polygon mesh models.INDEX TERMS Polygon mesh, virtual reality, perceptual quality, visual experience.

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“…For example, Karni and Gotsman [17] proposed combining the RMS geometric distance between corresponding vertices with the RMS distance of their Laplacian coordinates (which reflect a degree of smoothness of the surface). Lavoué [18] and Torkhani et al [19] proposed metrics based on local differences of curvature statistics, while Vasa and Rus [20] considered the dihedral angle differences. These metrics consider local variations of attribute values at vertex or edge level, which are then pooled into a global score.…”

Section: Introductionmentioning

confidence: 99%

“…Some authors also proposed quality assessment metrics for textured 3D mesh [23], [24], which integrate both texture and geometry information. Similar to the image quality metrics, some of these latter algorithms [19], [20], [22] integrate perceptually motivated mechanisms such as visual masking.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Quality Metrics for 3D Meshes: Towards an Optimal Multi-attribute Computational Model

Lavoué

Cheng

Basu

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-3D graphical data, commonly represented using triangular meshes, are deployed in a wide range of application processes including compression, filtering, watermarking, and simplification. These processes often introduce geometric distortions which affect the visual quality of the ultimate data visualization. In order to accurately evaluate perceptual impacts caused by the distortions, assessment metrics on 3D Mesh Visual Quality (MVQ) have been extensively discussed in the literature. Researchers recommended various metrics to predict the adverse effects that visual artifacts can have in applications. Most of these metrics are based on geometric attributes, conventional geometric distance, Laplacian coordinates, different types of curvature computation, and dihedral angles. We hypothesize that an optimal combination of multiple attributes associated with a 3D mesh surface can contribute to better perceptual prediction than single attributes used separately. In this paper, we use two user studies to validate our hypothesis. Our contributions are:(1) providing a detailed analysis of the most relevant geometric attributes for mesh quality assessment, and (2) introducing a new perceptual evaluation metric based on multiple attributes, with the optimal combination determined through machine learning techniques. Statistical quantitative analysis shows that our metric delivers better results than other state-of-the-art approaches. The proposed method is simple to implement and fast in execution. Moreover, our framework can easily be expanded to accommodate additional surface attributes.

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A Curvature Tensor Distance for Mesh Visual Quality Assessment

Cited by 38 publications

References 19 publications

Evaluation Metrics used in 3D Mesh Watermarking: A Study

Evaluation Metrics used in 3D Mesh Watermarking: A Study

Perceptual Quality and Visual Experience Analysis for Polygon Mesh on Different Display Devices

Perceptual Quality Metrics for 3D Meshes: Towards an Optimal Multi-attribute Computational Model

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