1. Bitstream- and hybrid-based video quality assessment for IPTV
Savvas Argyropoulos, Alexander Raake, Peter List, Colloquium on Quality of Experience in
Marie-Neige Garcia, Bernhard Feiten Multimedia Systems and Services,
Assessment of IP-based Applications, 23 November, Klagenfurt
Telekom Innovation Laboratories,
TU Berlin, Germany
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2. Video quality assessment
TV-Content
Transmission- Subjective
System quality-
rating
(T-V-) Estimated
Model quality index
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3. Quality of Experience
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4. Video quality assessment - Example
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5. Video quality assessment
Full-Reference
TV-Content
Transmission- Subjective
System quality-
rating
(T-V-) Estimated
Model quality index
Full-reference models
 ITU-T J.144
 SD
 no transmission errors
 ITU-T J.247
 VGA, CIF, QCIF
 ITU-T J.341
 HD J.341
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6. Video quality assessment
Reduced-Reference
TV-Content
Transmission- Subjective
System quality-
rating
Feature (T-V-) Estimated
extraction Model quality index
Reduced-reference models
 ITU-T J.143
 ITU-T J.246
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7. Video quality assessment
No-Reference bitstream models
TV-Content
Transmission- Subjective
System quality-
rating
(T-V-) Estimated
Model quality index
No-reference bitstream models
 ITU-T P.120X.Y
 QCIF- HD
 transmission errors
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8. Video quality assessment
No-Reference Hybrid (PVS+Bitstream)
TV-Content
Transmission- Subjective
System quality-
rating
(T-V-) Estimated
Model quality index
Hybrid models
 VQEG – Hybrid, Hybrid-JEG
 VGA, WVGA, HD
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9. Video quality assessment
Signal-based vs bitstream methods
Compressed, error-free Compressed, lossy video
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10. Multi-layer perspective Layer Example metric
user Picture Distortion
TV
... ...
video
set-top box
decoding
Frame Degrad. duration
... ...
de-
packetization
MPEG2-TS
payload
header
... RTP payload IP ... Packet Packet loss rate
UDP payload
IP payload Telekom Innovation Laboratories 10 10

11. Multi-layer model framework: T-V-model
Raake et al., IEEE SPM Nov. 2011
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12. P.120X.Y recommendations timeline
Jun. Dec. Apr. May. Sep.
2011 2012 2012 2012 2012
Training Model Test Model Draft
databases submission databases evaluation recommendation
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13. Bit stream video quality model
Video bitstream
Argyropoulos et al.,
IEEE QoMex 2011
Probe
Bitstream decoding
Visibility classifier
bitrate
Video quality assessment
ˆ
MOS
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14. Error visibility assessment
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15. Error visibility assessment
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16. Packet loss visibility classification based on probability estimates
DMOS of erroneous sequences encoded at 4Mbps
3.5
A
B
3 C
D
E
2.5 F
2
MOSclean - MOS
1.5
1
0.5
0
-0.5
0 10 20 30 40 50 60 70
Number of detected losses
Packet losses that are viewed only by a fraction of the viewers still have an effect on quality
Non detectable ≠ invisible
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17. Classification of packet loss visibility
6
invisible packet loss
4 visible packet loss
2
mvdiff
0
-2
-4
1
-6 0.8
0.5 0.6
0.6 0.7 0.4
0.8 0.9 0.2
1 0
dMB number of impaired pixels
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18. Bitstream video quality model
General model
 Additive model for distortions due to
QV  QCod  ItraV
compression and transmission error
Bitstream-based model
IcodV  f  f size , MBtype , ACcoefs , fps 
Freeze
ItraV  f IcodV , # Froz. frm 
Slicing
ItraV  f IcodV , vis, ErrProp,ErrDeg 
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19. Bit stream video quality model
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20. Bit stream video quality model – Extracted features
 ErrorProp: Number of frames affected by the packet loss (calculation based on frame
type)
 AvgMv: Average motion vector of the macroblocks that were lost (*)
 ResEnergy: Square of transform coefficients of the missing macroblocks
 MaxPartNr: Maximum number of partitions of the missing macroblocks
 EstError: Predicted error (in terms of MSE) due to the packet loss (in the frame where the
loss occurred)
 LostMbs: Number of impaired macroblocks that were lost due to the packet loss (in the
frame where the loss occurred)
 ErrorProp: Number of impaired pixels that were impaired due the packet loss and error
propagation (computed by all affected frame)
 AvgMvDiff: Average motion vector difference
(*) For the missing macroblocks, the information is obtained from the co-located
macroblocks in the previous correctly received frame
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21. Error maps estimation
Corrupted frames Binary error maps Error estimation
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22. Freezing degradation in P.1202.1
Freezing term for each freezing event i:
5
_
1 4.0 ∙
_ _
Motion term for each freezing event i:
∙
. ∑
+1.0
_
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23. Hybrid models – error concealment impact on subjective quality
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24. Q&A
http://www.aipa.tu-berlin.de/
Alexander.Raake@telekom.de
Savvas.Argyropoulos@telekom.de
Marie-Neige.Garcia@telekom.de
Peter.List@telekom.de
Bernhard.Feiten@telekom.de
Thank you!
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