Cmdm-Vac: Improving A Perceptual Quality Metric For 3D Graphics By Integrating A Visual Attention Complexity Measure

Yana Nehme (1) , Mona Abid (2, 3, 4) , Guillaume Lavoue (1) , Matthieu Perreira Da Silva (2, 4) , Patrick Le Callet (2, 4)

1 Origami - Origami
2 LS2N - Laboratoire des Sciences du Numérique de Nantes
3 EPUN - Ecole Polytechnique de l'Université de Nantes
4 LS2N - équipe IPI - Image Perception Interaction

Many objective quality metrics have been proposed over the years to automate the task of subjective quality assessment. However, few of them are designed for 3D graphical contents with appearance attributes; existing ones are based on geometry and color measures, yet they ignore the visual saliency of the objects. In this paper, we combined an optimal subset of geometry-based and color-based features, provided by a state-of-the-art quality metric for 3D colored meshes, with a visual attention complexity feature adapted to 3D graphics. The performance of our proposed new metric is evaluated on a dataset of 80 meshes with diffuse colors, generated from 5 source models corrupted by commonly used geometry and color distortions. With our proposed metric, we showed that the use of the attentional complexity feature brings a significant gain in performance and better stability.

Mots clés: Perceptual quality metric, 3D graphics, saliency, visual attention complexity, diffuse color; Perceptual quality metric, 3D graphics, saliency, visual attention complexity, diffuse color

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]

Yana Nehme, Mona Abid, Guillaume Lavoue, Matthieu Perreira Da Silva, Patrick Le Callet. Cmdm-Vac: Improving A Perceptual Quality Metric For 3D Graphics By Integrating A Visual Attention Complexity Measure. 2021 IEEE International Conference on Image Processing (ICIP), Sep 2021, Anchorage, France. pp.3368-3372, ⟨10.1109/ICIP42928.2021.9506662⟩. ⟨hal-03356806⟩