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No-reference video quality measurement: Added value of machine learning
Decebal Constantin Mocanu
, Jeevan Pokhrel
, Juan Pablo Garella
, Janne Seppänen
, Eirini Liotou
, Manish Narwaria
VTT Technical Research Centre of Finland
Eindhoven University of Technology (TU/e)
Universidad de la Republica
VTT (former employee or external)
National and Kapodistrian University of Athens
Dhirubhai Ambani Institute of Information and Communication Technology
Research output
:
Contribution to journal
›
Article
›
Scientific
›
peer-review
26
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Citations (Scopus)
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Keyphrases
Machine Learning
100%
Digital Terrestrial Television
100%
Broadcast Transmission
100%
Video Quality Assessment
100%
NR-IQA
100%
Prediction Accuracy
50%
Visual Quality
50%
Objective Assessment
50%
Mean Opinion Score
50%
Video Quality
50%
Video Delivery
50%
Video Content
50%
Delivery Chain
50%
No-reference
50%
Quality Estimator
50%
Video Clips
50%
Subjective Scoring
50%
Single Score
50%
Accuracy-based
50%
Subjective Variability
50%
Subjective Opinion
50%
Machine Learning Based
50%
Video Broadcasting
50%
Objective Video Quality
50%
Verification Study
50%
Further Analysis
50%
Television Broadcast
50%
Inter-observer Differences
50%
Scoring Process
50%
INIS
values
100%
solutions
100%
transmission
100%
machine learning
100%
prediction
100%
television
100%
performance
50%
applications
50%
chains
50%
delivery
50%
accuracy
50%
verification
50%
utilities
50%
Computer Science
Machine Learning
100%
Learning System
100%
Broadcast Television
50%
Delivery Chain
50%
Visual Quality
50%
Prediction Accuracy
50%
Broadcast Video
50%
Quality Objective
50%
Engineering
Broadcast Transmission
100%
Reference Video
100%
Learning System
100%
Mean Opinion Score
50%
Quality Objective
50%