Ms. Yaoyao Lin Profile

Yaoyao Lin

at Ningbo Univ

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Publications (2)

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Proceedings Article | 10 October 2020 Poster + Paper

VQA-CPC: a novel visual quality assessment metric of color point clouds

Lei Hua, Mei Yu, Gangyi Jiang, Zhouyan He, Yaoya Lin

Proceedings Volume 11550, 1155012 (2020) https://doi.org/10.1117/12.2573686

KEYWORDS: Clouds, Visualization, Distortion, 3D image processing, Image processing, Feature extraction, Information visualization, 3D metrology, Databases, Stereoscopy

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Color point clouds can provide users more realistic visual information and better immersive experience than traditional imaging techniques. How to evaluate the visual quality of color point clouds accurately is an important issue to be solved urgently. In this work, we propose a novel full reference metric, called as Visual Quality Assessment of Color Point Clouds (VQA-CPC). Starting from the geometry and texture of color point cloud, the proposed metric calculates the distances from color point cloud’s points to their geometric centroid and the distances from the texture coordinates of the points to texture centroid. Then, a measuring distortion strategy based on distortion measurement is designed and used to extract the features of color point cloud. Finally, the extracted geometric features and texture features are used to construct the feature vector and predict quality of the distorted color point cloud. Moreover, we construct a color point cloud database, called as NBU-PCD1.0, for verifying the effectiveness of the proposed metric. Experimental results show that the proposed VQA-CPC metric is better than the existing point cloud metrics.

Proceedings Article | 2 November 2018 Paper

Novel 3D mesh quality assessment method based on curvature analysis

Yaoyao Lin, Mei Yu, Gangyi Jiang, Yang Song, Hua Shao

Proceedings Volume 10817, 108170E (2018) https://doi.org/10.1117/12.2502185

KEYWORDS: 3D modeling, Eye models, Visualization, Databases, Data modeling, 3D image processing, Visual process modeling, Performance modeling

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With the wide applications of three-dimensional (3D) mesh model in digital entertainment, animation, virtual reality and other fields, there are more and more processing techniques for 3D mesh models, including watermarking, compression, and simplification. These processing techniques will inevitably lead to various distortions in 3D mesh. Thus, it is necessary to design effective tools for 3D mesh quality assessment. In this work, considering that the curvature can measure concavity and convexity of surface well, and the human eyes are also very sensitive to the change of curvature, we propose a new objective 3D mesh quality assessment method. Curvature features are used to evaluate the visual difference between the reference and distorted meshes. Firstly, the Gaussian curvature and the mean curvature on the vertices of the reference and distorted meshes are calculated, and then the correlation function is used to measure the correlation coefficient of these meshes. In this case, the degree of degradation of the distorted mesh can be well represented. Finally, the Support Vector Regression model is used to fuse the two features and the objective quality score could be obtained. The proposed method is compared with seven existing 3D mesh quality assessment methods. Experimental results on the LIRIS_EPFL_GenPurpose Database show that the PLCC and SROCC of the proposed method are increased by 13.60% and 6.23%, compared with the best results of the seven representative methods. It implies that the proposed model has stronger consistency with the subjective visual perception of human eyes.

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