From 2D to Stereoscopic-3D Visual Saliency: Revisiting Psychophysical Methods and Computational Modeling
Junle Wang · HAL (Le Centre pour la Communication Scientifique Directe) · 2012
Visual attention is one of the most important mechanisms deployed in the human visual system to reduce the amount of information that our brain needs to process. An increasing amount of efforts are being dedicated in the studies of visual attention, particularly in computational modeling of visual attention. In this thesis, we present studies focusing on several aspects of the research of visual attention. Our works can be mainly classified into two parts. The first part concerns ground truths used in the studies related to visual attention ; the second part contains studies related to the modeling of visual attention for Stereoscopic 3D (S-3D) viewing condition. In the first part, our work starts with identifying the reliability of FDM from different eye-tracking databases. Then we quantitatively identify the similarities and difference between fixation density maps and visual importance map, which have been two widely used ground truth for attention-related applications. Next, to solve the problem of lacking ground truth in the community of 3D visual attention modeling, we conduct a binocular eye-tracking experiment to create a new eye-tracking database for S-3D images. In the second part, we start with examining the impact of depth on visual attention in S-3D viewing condition. We firstly introduce a so-called "depth-bias" in the viewing of synthetic S-3D content on planar stereoscopic display. Then, we extend our study from synthetic stimuli to natural content S-3D images. We propose a depth-saliency-based model of 3D visual attention, which relies on depth contrast of the scene. Two different ways of applying depth information in S-3D visual attention model are also compared in our study. Next, we study the difference of center-bias between 2D and S-3D viewing conditions, and further integrate the center-bias with S-3D visual attention modeling. At the end, based on the assumption that visual attention can be used for improving Quality of Experience of 3D-TV when collaborating with blur, we study the influence of blur on depth perception and blur's relationship with binocular disparity.