EXFO EXpert IPTV Test Tools (FTB-200v2) User Manual
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video at a given bitrate under no loss condition, as well as under loss conditions as described
below. VQmon/HD automatically adjusts the video stream effective bitrate based on the GOP
structure of the encoded video stream.
A.1.3
Impact of Subjective Factors on Perceptual Quality
The degree to which viewers find video impairments annoying—or notice them at
all— depends in part on the severity and duration of the impairment events, but also on
certain inherent characteristics of human perception. The same type of impairment may be
extremely irritating or barely noticed, depending on factors such as the scene content when
the error takes place, and whether it occurs alone or simultaneously with other impairments.
A.1.3.1
Video Content
The visibility of video problems depends partially on the scene content; for example,
frame freezes tend to be much more noticeable in sequences containing high levels of motion
than in relatively static scenes, such as footage of a television news anchor. VQmon/HD
performs content and scene analysis, detecting levels of detail, motion, and panning, and can
detect and provide alerts for content problems such as noise/snow and frame freezes. Scene
analysis data is leveraged to increase the accuracy of VQmon/HD’s estimated perceptual
quality scores.
A.1.3.2
Temporal Phenomena: Reaction Time, Masking and Recency Effects
Perceptual quality is affected somewhat by a natural delay in human reaction time
when impairments occur, or when quality improves after a period of degradation; i.e., the
viewer’s reaction to either event is not immediate. As with audio, when two or more
impairments occur simultaneously (or in rapid succession), there can be a “masking” effect
that affects the way viewers perceive the severity of quality degradation. In addition, a
“recency” phenomenon exists, wherein viewers tend to perceive impairments as more severe
when they occurred recently, but are willing to “forgive” them to some extent as time passes.
VQmon/HD’s quality analysis algorithm considers these temporal phenomena in order to
calculate perceptual quality scores that correlate as accurately as possible to scores obtained
from subjective tests of live viewers.