Post on 06-Jan-2016
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transcript
>> HIGHERVIEW
Team:
A. Sasse
J. D. McCarthy
D. Miras
J. Riegelsberger
Presentation to UCL Network Group: 3rd March 2004
>> Sharp or smooth?Comparing the effects of quantization vs. frame rate for streamed video.
J.D. McCarthy
M. A. Sasse
D. Miras
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>> motivation
> Existing QOS policies conflict with experimental evidence.
> No previous studies manipulating frame quality in conjunction with frame rate.
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>> motivation
> IBM QOS policy (2003)“recommends reducing DCT coefficients rather than frame rate for Sports coverage, as “the priority for smooth video is higher than the priority for frame quality”
> Apteker et al. (1995) > Sport coverage relatively insensitive to reductions in frame rate.
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>> methodology
> Continuously change video quality while users are watching.
> Continuously record user’s perception.
> Discover the relationship between signal quality and perceived quality.
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>> which measure?
> Mean Opinion Score (MOS)– 8-10 second clips – single camera angle
– rate quality on a 5 point Likert scale.
> Limitations– Doesn’t measure continuous quality variations.– Poor measure for streamed video quality.– Doesn’t measure acceptability.
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>> which measure?
> SSCQE – The single stimulus continuous quality evaluation (SSCQE)– using a slider to indicate quality continuously.
> Limitations– Too demanding for users performing real tasks.– Doesn’t measure service acceptability.
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>> acceptability?
> Is a MOS of 3.5 acceptable to users?
> What about an SSCQE rating of 70?
> Service dependent?
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>> our approach
> Focus on a specific service.> Ask users to say when the service is
acceptable / unacceptable.
> Advantages– Can be used with continuous streams
– Easy for users to understand
– Less disruptive
– Relevant to service providers
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>> methodology
> Continuously change video quality while users are watching.
> Continuously record user’s perception.
> Discover the relationship between signal quality and perceived quality.
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>> “method of limits”
unacceptable
acceptable
low quality
high quality
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>> “method of limits”
unacceptable
acceptable
low quality
high quality
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>> “method of limits”
unacceptable
acceptable
low quality
high quality
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>> service functions
unacceptable
acceptable
low quality
high quality
Pr (acceptable)
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>> service functions
unacceptable
acceptable
low quality
high quality
Pr (acceptable) ITU BT.500-11
Logistic Function
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>> service functions
unacceptable
acceptable
frame rate
?
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>> service functions
unacceptable
acceptable
frame quality
?
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>> two studies
> Study 1– CIF video viewed on a desktop. – Acceptability ratings.– Eye movements.
> Study 2– QCIF video viewed on an iPAQ.– Acceptability ratings.– Qualitative interviews.
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>> video material
> Football match– Arsenal vs Man. United (2002)
• 3 source clips.
– [A] Match intro and opening 3 minutes of play– [B] Highlights of Manchester United chances– [C] Highlights of Arsenal chances, final whistle and
Arsenal celebration.
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>> participants
> Study 1– 41 football fans.
– 59% watched at least once a week
– 88% supported a football team.
– 51% supported Arsenal or Man U.
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>> participants
> Study 2– 37 football fans.
– 65% watched at least once a week
– 84% supported a football team.
– 34 % supported Arsenal or Man U.
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>> design
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>> study 1 - results
fps
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>> study 1 - results
quant
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>> study 1 - results
fps +
quant
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>> study 1 - results
gaze
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>> study 1 - summary
> Acceptability insensitive to frame rate.
> Acceptability sensitive to quantization.
> Critical values:– Quantisation = 8– Frame rate = 6
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>> study 2 - results
fps
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>> study 2 - results
quant
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>> study 2 - results
fps +
quant
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>> bandwidth?
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>> bandwidth?
Critical
Values
(Clip B)
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>> qualitative comments
– 84%, recognising players was impossible.
– 65% had problems following the ball.
– 35% said close up shots fine - but long distant shots poor.
– 21% said jerky movement was a problem.
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>> qualitative comments
“I’d rather have jerky video and
better quality pictures”
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>> study 2 - summary
> Acceptability insensitive to frame rate.
> Acceptability sensitive to quantization.
> Critical values:– Quantisation = 4– Frame rate = 6
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>> conclusions
> Limitations– Network effects not factored in.
> Substantive– High motion does not need high frame
rate! – Important task relevant information is lost
with poor frame quality.
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>> conclusions
> Methodological– Binary acceptability rating
• continuous• easy to understand• doesn’t disrupt task
– “Method of limits” produces robust replicable service functions.
>> Sharp or smooth?Comparing the effects of quantization vs. frame rate for streamed video.
J.D. McCarthy
M. A. Sasse
D. Miras