A new mesh visual quality metric using saliency weighting-based pooling strategy

Feng, Xiang; Wan, Wanggen; Xu, Richard Yi Da; Perry, Stuart; Zhu, Song; Liu, Zexin

doi:10.1016/j.gmod.2018.07.001

Cited by 4 publications

(2 citation statements)

References 36 publications

(82 reference statements)

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“…Niu et al 18 discusses the use of Laplace–Beltrami eigen functions that are invariant to rigid transformations to extract salient points representing distinctive regions computed based on specific criteria, including clustering and geodesic distance computations. Feng et al 19 presented a novel mesh visual quality metric that integrated saliency considerations to estimate local distortions in the mesh. Li et al 20 performed multiresolution 3D wavelet analysis, Laplacian smoothing and normalization and Wavelet Coefficient for watermarks embedding and extraction.…”

Section: Literature Surveymentioning

confidence: 99%

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Narendra,

Valarmathi,

Anbarasi

et al. 2024

Sci Rep

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Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg–Marquardt (NCDG–LV), for distortion detection and recovery using salient point detection. In this method, the salient points are selected using the Nearest Centroid and Discrete Gaussian Geometric (NC–DGG) salient point detection model. Map segmentation is applied to the 3D Mesh model to segment into distinct sub regions according to the selected salient points. Finally, the watermark is embedded by employing the Multi-function Barycenter into each spatially selected and segmented region. In the extraction process, the embedded 3D Mesh image is extracted from each re-segmented region by means of Levenberg–Marquardt Deep Neural Network Watermark Extraction. In the authentication stage, watermark bits are extracted by analyzing the geometry via Levenberg–Marquardt back-propagation. Based on a performance evaluation, the proposed method exhibits high imperceptibility and tolerance against attacks, such as smoothing, cropping, translation, and rotation. The experimental results further demonstrate that the proposed method is superior in terms of salient point detection time, distortion rate, true positive rate, peak signal to noise ratio, bit error rate, and root mean square error compared to the state-of-the-art methods.

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Section: Literature Surveymentioning

confidence: 99%

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Narendra,

Valarmathi,

Anbarasi

et al. 2024

Sci Rep

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“…Regarding model-based full-reference 3D mesh visual metrics that integrate 3D visual saliency in their pipeline, we can notice that there are relatively fewer works in the literature in comparison with 2D image metrics [14][15][16][17][18]. Indeed, only one approach was proposed [19]. This approach predicts the final quality score based on a spatial pooling strategy, visual saliency weighting and statistical descriptors obtained from a distortion map [12] along with a support vector regression model.…”

Section: D Mesh Visual Quality Assessment Metrics In the State-of-thmentioning

confidence: 99%

A Genetically Based Combination of Visual Saliency and Roughness for FR 3D Mesh Quality Assessment: A Statistical Study

Nouri

Charrier

Lézoray

2020

The Computer Journal

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In this paper, we present a full-reference quality assessment metric based on the information of visual saliency. The saliency information is provided under the form of degrees associated to each vertex of the surface mesh. From these degrees, statistical attributes reflecting the structures of the reference and distorted meshes are computed. These are used by four comparisons functions genetically optimized that quantify the structure differences between a reference and a distorted mesh. We also present a statistical comparison study of six full-reference quality assessment metrics for 3D meshes. We compare the objective metrics results with humans subjective scores of quality considering the 3D meshes in one hand and the distorsion types in the other hand. Also, we show which metrics are statistically superior to their counterparts. For these comparisons we use the Spearman Rank Ordered Correlation Coefficient and the hypothetic test of Student (ttest). To attest the pertinence of the proposed approach, a comparison with a ground truth saliency and an application associated to the assessment of the visual rendering of smoothing algorithms are presented. Experimental results show that the proposed metric is very competitive with the state-of-the-art.

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Saliency detection for large-scale mesh decimation

Anjos¹,

Roberts²,

Allen³

et al. 2023

Computers & Graphics

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A new mesh visual quality metric using saliency weighting-based pooling strategy

Cited by 4 publications

References 36 publications

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

A Genetically Based Combination of Visual Saliency and Roughness for FR 3D Mesh Quality Assessment: A Statistical Study

Saliency detection for large-scale mesh decimation

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