Abstract
Several metrics have been proposed to assess the visual quality of 3D triangular meshes during the last decade. In this paper, we propose a mesh visual quality metric by integrating mesh saliency into mesh visual quality assessment. We use the Tensor-based Perceptual Distance Measure metric to estimate the local distortions for the mesh, and pool local distortions into a quality score using a saliency weighting-based pooling strategy. Three well-known mesh saliency detection methods are used to demonstrate the superiority and effectiveness of our metric. Experimental results show that our metric with any of three saliency maps performs better than state-of-the-art metrics on the LIRIS/EPFL general-purpose database. We generate a synthetic saliency map by assembling salient regions from individual saliency maps. Experimental results reveal that the synthetic saliency map achieves better performance than individual saliency maps, and the performance gain is closely correlated with the similarity between the individual saliency maps.
Highlights
With the advance of 3D acquisition techniques, 3D triangular mesh has become a standard digital representation of 3D object surface and is widely used in various human centered applications
We evaluate the performance of our metric by measuring the correlation between the quality scores and Mean Opinion Score (MOS) with two coefficients: Pearson linear correlation coefficient (PLCC) that measures the prediction accuracy of quality metric and Spearman rank-order correlation coefficient (SROCC)
We have proposed a mesh visual quality high saliency at the #1, #2, #3, and #4 regions as shown metric using a saliency weighting-based pooling strategy
Summary
With the advance of 3D acquisition techniques, 3D triangular mesh has become a standard digital representation of 3D object surface and is widely used in various human centered applications. Many computational saliency methods [8,9,10,11,12] have been proposed to detect perceptually important regions where human visual attention is focused on the mesh. In [13,14,15,16,17,18], either visual attention or computational visual saliency was incorporated in image quality metrics to improve the performance based on the assumption that distortions occurring in more salient areas of an image are more visible and more annoying, which was verified by the experimental results. Several works [13,14,15,16,17] have been done to investigate the added value of including visual attention or computational visual saliency in IQMs. Moorthy et al [13] proposed weighting local quality measurement by visual fixation and demonstrated improved performance for image quality assessment. Experimental results show that the synthetic saliency map achieves better performance than individual saliency maps when used in our metric, and the performance gain is closely correlated with the similarity between the individual saliency maps
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