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.