2 research outputs found
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
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