Exploring the efficiency of image metric for assessing the visual quality of 3D mesh model

Recent developments in 3D graphics technology have led to extensive processes on 3D meshes(e.g.,compression,simplification,transmission and watermarking),these processes unavoidably cause the visual perceptual degradation of the 3D objects.The existing mesh visual quality evaluation metrics either require topology constrain or fail to reflect the perceived visual quality.Meanwhile,for the 3D objects that are observed on 2D screens by the users,it is reasonable to apply image metric to assess the distortion caused by mesh simplification.We attempt to explore the efficiency of image metric for assessing the visual fidelity of the simplified 3D model in this paper.For this purpose,several latest and most effective image metrics,2D snapshots,number and pooling algorithms are involved in our study,and finally tested on the IEETA simplification database.The statistical data allow the researcher to select the optimal parameter for this image-based mesh visual quality assessment and provide a new perspective for the design and performance assessment of mesh simplification algorithms.

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

In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become 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 the dis- tortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the dis- tortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a 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.

The state-of-the-art in computer generated holography for 3D display

Holographic displays have the promise to be the ultimate 3D display technology,able to account for all visual cues.Recent advances in photonics and electronics gave rise to high-resolution holographic display prototypes,indicating that they may become widely available in the near future.One major challenge in driving those display systems is computational:computer generated holography(CGH)consists of numerically simulating diffraction,which is very computationally intensive.Our goal in this paper is to give a broad overview of the state-of-the-art in CGH.We make a classification of modern CGH algorithms,we describe different algorithmic CGH acceleration techniques,discuss the latest dedicated hardware solutions and indicate how to evaluate the perceptual quality of CGH.We summarize our findings,discuss remaining challenges and make projections on the future of CGH.