4 research outputs found
3D Video Quality Metric for Mobile Applications
In this paper, we propose a new full-reference quality metric for mobile 3D
content. Our method is modeled around the Human Visual System, fusing the
information of both left and right channels, considering color components, the
cyclopean views of the two videos and disparity. Our method is assessing the
quality of 3D videos displayed on a mobile 3DTV, taking into account the effect
of resolution, distance from the viewers eyes, and dimensions of the mobile
display. Performance evaluations showed that our mobile 3D quality metric
monitors the degradation of quality caused by several representative types of
distortion with 82 percent correlation with results of subjective tests, an
accuracy much better than that of the state of the art mobile 3D quality
metric.Comment: arXiv admin note: substantial text overlap with arXiv:1803.04624;
text overlap with arXiv:1803.04832 and arXiv:1803.0483
An Efficient Human Visual System Based Quality Metric for 3D Video
Stereoscopic video technologies have been introduced to the consumer market
in the past few years. A key factor in designing a 3D system is to understand
how different visual cues and distortions affect the perceptual quality of
stereoscopic video. The ultimate way to assess 3D video quality is through
subjective tests. However, subjective evaluation is time consuming, expensive,
and in some cases not possible. The other solution is developing objective
quality metrics, which attempt to model the Human Visual System (HVS) in order
to assess perceptual quality. Although several 2D quality metrics have been
proposed for still images and videos, in the case of 3D efforts are only at the
initial stages. In this paper, we propose a new full-reference quality metric
for 3D content. Our method mimics HVS by fusing information of both the left
and right views to construct the cyclopean view, as well as taking to account
the sensitivity of HVS to contrast and the disparity of the views. In addition,
a temporal pooling strategy is utilized to address the effect of temporal
variations of the quality in the video. Performance evaluations showed that our
3D quality metric quantifies quality degradation caused by several
representative types of distortions very accurately, with Pearson correlation
coefficient of 90.8 %, a competitive performance compared to the
state-of-the-art 3D quality metrics
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