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trying to model each individual distortion. Further, if one does choose to model each
distortion individually, a method to study the effect of multiple distortions must be
undertaken. Again, this is a combinatorially challenging problem.
A majority of algorithms seek to model spatial-distortions alone and even though
some methods include elementary temporal features, a wholesome approach to NR
VQA should involve a spatio-temporal distortion model. Further, in most cases a
majority of the design decisions are far removed from human vision processing. It
is imperative as researchers that we keep in mind that the ultimate receiver is the
human and hence understanding and incorporating HVS properties in an algorithm
is of essence. Finally, even though we listed statistical measures to evaluate per-
formance, researchers are working on alternative methods to quantify performance
[81, 82, 83].
References
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Essential Guide to Video Processing. Academic Press, London (2009)
5. Moorthy, A.K., Seshadrinathan, K., Bovik, A.C.: Digital Video Quality Assessment
Algorithms. Springer, Heidelberg (2009)
6. Final report from the video quality experts group on the validation of objective quality
metrics for video quality assessment,
http://www.its.bldrdoc.gov/vqeg/projects/frtv_phaseI
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The University of Texas at Austin (2008)
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(MPEG-2) (1994)
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13. Live wireless video database (2009),
http://live.ece.utexas.edu/research/quality/live wireless
live wireless video.html
14. Live video database (2009),
http://live.ece.utexas.edu/research/quality/
live video.html
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tive and objective quality assessment of video. IEEE Transactions on Image Processing
(to appear)
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