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Other literature type . 2016
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Article . 2018
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3D video quality assessment

Authors: Banitalebi Dehkordi, Amin;

3D video quality assessment

Abstract

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.

PhD Thesis, UBC, 2015

Countries
Mexico, Canada, United States, Canada
Keywords

FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 700

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green