Objective quality measures based on machinelearning (ML) require fewer computations andare less affected by inaccuracies in the HVSmodels. But they may also yield less transparentquality predictions when the ML responses aredifficult to interpret. The absence ofinterpretability may disguise seriousvulnerabilities in the design of the objectivequality measure.
Barri, A, Dooms, A & Schelkens, P 2014, Reliably combining quality indicators. vol. 1, 2 edn, Video Quality Experts Group, VQEG eLetter. <ftp://vqeg.its.bldrdoc.gov/eLetter/Issues/VQEG_eLetter_vol01_issue2.pdf>
Barri, A., Dooms, A., & Schelkens, P. (2014). Reliably combining quality indicators. (2 ed.) Video Quality Experts Group. ftp://vqeg.its.bldrdoc.gov/eLetter/Issues/VQEG_eLetter_vol01_issue2.pdf
@book{4407a6a1060f41d1943013b240d40a83,
title = "Reliably combining quality indicators",
abstract = "Objective quality measures based on machinelearning (ML) require fewer computations andare less affected by inaccuracies in the HVSmodels. But they may also yield less transparentquality predictions when the ML responses aredifficult to interpret. The absence ofinterpretability may disguise seriousvulnerabilities in the design of the objectivequality measure.",
author = "Adriaan Barri and Ann Dooms and Peter Schelkens",
year = "2014",
month = dec,
language = "English",
volume = "1",
publisher = "Video Quality Experts Group",
edition = "2",
}