Perceptual Characterization of 3D Graphical Contents based on Attention Complexity Measures

Mona Abid (1, 2) , Matthieu Perreira Da Silva (3) , Patrick Le Callet (1, 2)

1 LS2N - équipe IPI - Image Perception Interaction
2 LS2N - Laboratoire des Sciences du Numérique de Nantes
3 L3I - Laboratoire Informatique, Image et Interaction - EA 2118

This paper provides insights on how to perceptually characterize colored 3D Graphical Contents (3DGC). In this study, pre-defined viewpoints were considered to render static graphical objects. For perceptual characterization, we used visual attention complexity (VAC) measures. Considering a view-based approach to exploit the perceived information, an eye-tracking experiment was conducted using colored graphical objects. Based on the collected gaze data, we revised the VAC measure, suggested in 2D imaging context, and adapted it to 3DGC. We also provided an objective predictor that highly mimics the experimental attentional complexity information. This predictor can be useful in Quality of Experience (QoE) studies: to balance content selection when benchmarking 3DGC processing techniques (e.g., rendering, coding, streaming, etc.) for human panel studies or ad hoc key performance indicator, and also to optimize the user's QoE when rendering such contents.

Domaines: Informatique [cs]; Traitement des images [eess.IV]

Mona Abid, Matthieu Perreira Da Silva, Patrick Le Callet. Perceptual Characterization of 3D Graphical Contents based on Attention Complexity Measures. Workshop QoEVMA ‘20, part of MM '20: The 28th ACM International Conference on Multimedia, Oct 2020, Seattle WA USA, United States. pp.31-36, ⟨10.1145/3423328.3423498⟩. ⟨hal-02971538⟩