A novel spatial pooling method for 3D mesh quality assessment based on percentile weighting strategy

Feng, Xiang; Wan, Wanggen; Xu, Richard Yi Da; Perry, Stuart; Li, Pengfei; Zhu, Song

doi:10.1016/j.cag.2018.04.005

Cited by 8 publications

(9 citation statements)

References 25 publications

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“…Higher values of M imply a higher weighting towards large distortions. In many studies, M is chosen arbitrarily, but tends to have a value between 2 and 3, [48]. Note that setting M = 1 reduces Minkowski pooling to the arithmetic mean,…”

Section: Visual Impact Estimationmentioning

confidence: 99%

“…Minkowski pooling is used to approximate visual impact in many full-reference VQA models. While Minkowski pooling somewhat accounts for human perceptual behaviour, this method has its drawbacks: Firstly, Minkowski pooling treats all distortions as independent, when in reality, the effects of multiple distortions over-lap to influence visual quality perceptions, [48]. Second, Minkowski pooling assumes the visual impact of a distortion is directly related to the extent of geometric displacement with no other compounding factors.…”

Section: Visual Impact Estimationmentioning

confidence: 99%

“…Visual saliency is a major area of research in both computer vision and neuroscience, [50]. Saliency is occasionally incorporated into mesh VQA models as a weighting factor, such that perceptual metrics are weighted more heavily in regions that contribute more to visual quality ( [27], [35], [40], [42] and [48]). Saliency has been approximated in several studies as a function of curvature ( [34], [40] and [44]) or surface roughness, [42].…”

Section: Saliency Estimationmentioning

confidence: 99%

“…Methods based on perceptual metrics tend to operate in a three-stage process (see [34] and [48]). First, the method calculates the value of one or more metrics for every point (polygon or vertex, depending on the metric in question) on the mesh.…”

Section: Perceptual Quality Metricsmentioning

confidence: 99%

“…Second, Minkowski pooling assumes the visual impact of a distortion is directly related to the extent of geometric displacement with no other compounding factors. Later models aim to account for this in several ways: Feng et al [48], use a novel weighting method based on polygon surface area. This method is seen to overemphasise the impact of roughness however, as rough areas necessarily contain smaller polygons than smooth areas.…”

Section: Visual Impact Estimationmentioning

confidence: 99%

See 4 more Smart Citations

Automated Visual Quality Assessment for Virtual and Augmented Reality based Digital Twins

Roullier,

McQuade,

Bower

et al. 2023

Preprint

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Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce a novel set of decimation algorithms to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4,000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25,000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.

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Section: Visual Impact Estimationmentioning

confidence: 99%

Section: Visual Impact Estimationmentioning

confidence: 99%

Section: Saliency Estimationmentioning

confidence: 99%

Section: Perceptual Quality Metricsmentioning

confidence: 99%

Section: Visual Impact Estimationmentioning

confidence: 99%

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Automated Visual Quality Assessment for Virtual and Augmented Reality based Digital Twins

Roullier,

McQuade,

Bower

et al. 2023

Preprint

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No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling

Abouelaziz

Chetouani

Hassouni

et al. 2020

Pattern Recognition

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Learning Graph Convolutional Network for Blind Mesh Visual Quality Assessment

Abouelaziz¹,

Chetouani

Cherifi

et al. 2021

IEEE Access

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This paper proposes a new method for blind mesh visual quality assessment (MVQA) based on a graph convolutional network. For that, we address the node classification problem to predict the perceived visual quality. First, two matrices represent the 3D mesh: a graph adjacency matrix and a feature matrix. Both matrices are used as input to a shallow graph convolutional network. The network consists of two convolutional layers followed by a max-pooling layer to provide the final feature representation. With this structure, the Softmax classifier predicts the quality score category without the reference mesh's availability. Experiments are conducted on four publicly available databases constructed explicitly for the mesh quality assessment task. We investigate several perceptual and visual features to select the most effective combination. Comparisons with the state-of-the-art alternative methods show the effectiveness of the proposed framework. INDEX TERMSMesh visual quality assessment, graph convolutional networks, mesh graph representation, geometric attributes. Reference (NR) or Blind methods, no information about the reference is available [8]-[12]. Finally, in Reduced-Reference (RR) methods only part of the reference is available (i.e., features extracted from the reference) [13]-[17]. Most quality evaluation methods rely on the quality prediction technical tool and the feature type such as geometric (angles, curvature), topological (mesh connection), spatial (vertices and edges), frequency (Wavelet Transform [66], 21 Discrete cosine transform [67]), spectral (Laplace-Beltrami 22 operator [68]), etc. 23 One can distinguish two main categories of methods for 24 mesh quality assessment. The first category is based on 25 logistic regression to estimate the quality scores. A plethora 26 of such methods have been proposed in the literature and 27 have shown good performances [18]-[20], [25], [27]-[30]. 28 The second category relies on machine learning to predict 29 quality [31], [32]. More precisely, quality score estimation 30 is tackled into a classification context, or eventually, re-31 gression or regression by classification. In a previous work, 32 we have been the first to investigate Convolutional Neural 33 Network (CNN) as one of the most recent and relevant deep 34 learning tools to estimate the perceived visual quality of 3D 35 meshes [33]. 36 Intuitively, to handle simply CNN in our context, 2D im-37 ages are rendered from multiple views of the 3D mesh. Then, 38 each image is split into small patches, which are learned 39 to a convolutional neural network. As done for 2D images 40

show abstract

A novel spatial pooling method for 3D mesh quality assessment based on percentile weighting strategy

Cited by 8 publications

References 25 publications

Automated Visual Quality Assessment for Virtual and Augmented Reality based Digital Twins

Automated Visual Quality Assessment for Virtual and Augmented Reality based Digital Twins

No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling

Learning Graph Convolutional Network for Blind Mesh Visual Quality Assessment

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