2 research outputs found
A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video (LBVS-3D)
Over the past decade, many computational saliency prediction models have been
proposed for 2D images and videos. Considering that the human visual system has
evolved in a natural 3D environment, it is only natural to want to design
visual attention models for 3D content. Existing monocular saliency models are
not able to accurately predict the attentive regions when applied to 3D
image/video content, as they do not incorporate depth information. This paper
explores stereoscopic video saliency prediction by exploiting both low-level
attributes such as brightness, color, texture, orientation, motion, and depth,
as well as high-level cues such as face, person, vehicle, animal, text, and
horizon. Our model starts with a rough segmentation and quantifies several
intuitive observations such as the effects of visual discomfort level, depth
abruptness, motion acceleration, elements of surprise, size and compactness of
the salient regions, and emphasizing only a few salient objects in a scene. A
new fovea-based model of spatial distance between the image regions is adopted
for considering local and global feature calculations. To efficiently fuse the
conspicuity maps generated by our method to one single saliency map that is
highly correlated with the eye-fixation data, a random forest based algorithm
is utilized. The performance of the proposed saliency model is evaluated
against the results of an eye-tracking experiment, which involved 24 subjects
and an in-house database of 61 captured stereoscopic videos. Our stereo video
database as well as the eye-tracking data are publicly available along with
this paper. Experiment results show that the proposed saliency prediction
method achieves competitive performance compared to the state-of-the-art
approaches
3D Video Quality Assessment
A key factor in designing 3D systems is to understand how different visual
cues and distortions affect the perceptual quality of 3D video. The ultimate
way to assess video quality is through subjective tests. However, subjective
evaluation is time consuming, expensive, and in most cases not even possible.
An alternative solution is objective quality metrics, which attempt to model
the Human Visual System (HVS) in order to assess the perceptual quality. The
potential of 3D technology to significantly improve the immersiveness of video
content has been hampered by the difficulty of objectively assessing Quality of
Experience (QoE). A no-reference (NR) objective 3D quality metric, which could
help determine capturing parameters and improve playback perceptual quality,
would be welcomed by camera and display manufactures. Network providers would
embrace a full-reference (FR) 3D quality metric, as they could use it to ensure
efficient QoE-based resource management during compression and Quality of
Service (QoS) during transmission.Comment: PhD Thesis, UBC, 201