Standardization activities for non-intrusive quality ... · Telekom Innovation Laboratories...

Telekom Innovation Laboratories

Standardization activities for non-intrusive quality monitoring of multimedia services

Alexander Raake, Marie-Neige Garcia, Savvas Argyropoulos, Michal SoloduchaPeter List, Bernhard FeitenTelekom Innovation Laboratories, TU Berlin, Germany

ETSI STQ Workshop, Vienna, 27-28 Nov. 2012

Drawing: Sandra Buchmüller


Transmission-System

Source signal(SRC)

Bitstream / Parameters

Monitoring, a/v P.NAMS (Q.14/12) P.NBAMS (Q.14/12)Monitoring, speech P.564 G.107 (E-model)Planning G.107 (Q.8/12) G.OMVAS (Q.13/12)

G.1070 (videotelephony)

Standardized modelsBitstream/parametric

2

Model Estimatedquality index

Subjectivequality-rating

Diagnostic information


Telekom-Video-model (T--V-model): Multi-layer framework

• ITU-T SG12 model submission: Dec. 2011 – P.NAMS (audiovisual) – P.NBAMS (video-only)

• Consent on P.NAMS and P.NBAMS: Sept. 7, 2012 3

Mod

els

(Raake et al., IEEE SPM 2011)


Monitoring model standardization activitiesITU-T SG12, Question Q.14/12: P.NAMS, P.NBAMS

• (MPEG2-TS)/(RTP)/UDP/IP– Lower Resolution (LR: QCIF, QVGA, HVGA), Higher Resolution (HR: SD, HD)• P.NAMS – Packet-header audiovisual quality monitoring P.1201

– Audio MOS, Video MOS, Audiovisual MOS (weights during validation: 0.3/0.3/0.4)

– 7 participants(DTAG, Ericsson, Huawei, Netscout, NTT, Telchemy, Yonsei)

• P.NBAMS – Bitstream video quality monitoring P.1202– Video quality (later audio-part from P.NAMS)– 7 participants (DTAG, Ericsson, Huawei, Netscout, Technicolor, Telchemy, Yonsei)– Two modes: Mode 1 (parsing) & Mode 2 (decoding)

• Model submission 14 Dec. 2011• Standards consented 7 Sept. 2012

4


Controlled assessment procedureP.NAMS, P.NBAMS: End-to-end video transmission chain

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Controlled assessment procedureExample P.NAMS, P.NBAMS: Subjective test databases

Training• 15 (official) training databases

Validation• 24 validation databases

6

Audio Video Audiovisual

LR 1 3 2

HR 1 5 3

Audio Video Audiovisual

LR 2 4 4

HR 2 8 4


Example P.1201.2 – P.NAMS-HR encrypted streams

7

T-labs/DTAG work see:Argyropoulos et al. 2010-2011,Garcia et al. 2007-2012,Raake et al. 2008-2011

Mod

els

(Under study)

P.1202.1

P.1201.1P.1201.2

(Under study)


Impairment factors: Quality-related counterpart of technical degradations “Additive” on perceptual quality rating scale

Parametric video quality model – T-V-model

)1log( 10 bppdpSEQbbItra

ItraIcodQvoQv

4321 )exp( aSabppaaIcod I No loss:

Loss, PLC = freezing:

Loss, PLC = slicing: )1log()( 10 Icod

xwpSEQszcIcodcItra

frameRateresolutionbitratebpp

610

pixelper bits:bpp

SI: transformed I-frame size

8

dpSEQ: total freezing

xwpSEQsz: spatio-temporal slicingdegradation

Base model

Qvo: best possible quality

(Garcia & Raake, QoMEX 2011; ITU-T Rec. P.1201.2)


Slicing parameter xwpSEQ

xwpSEQsz: magnitude (spatial extent and duration) of loss degradation

9

N

kk

N

kkk

T

TxlxwpSEQ

1

1

k

N

iiki

k T

tTxlxl

1

)(

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xlk Loss degradation density (per GOP k)xli Initial loss degradation of frame i ti Time of loss event vs. GOP-startTk Duration GOP k

Note: corrected for slice-size!

(Garcia & Raake, QoMEX 2011; Raake et al. 2011; ITU-T Rec. P.1201.2)

Telekom Innovation Laboratories 10

Slicing parameter xwpSEQ

10

(Garcia & Raake, QoMEX 2011)

R Video quality on T-V-Model scalePpl Packet loss percentagexwpSEQ Spatial-temporal measure of slicing degradation

(no content-correction)

(Garcia 2012, unpublished)

Qua

lity

Qua

lity

Still a problem: content-dependency



Content-complexity & error-propagationInference from GOP-analysis

Desired modelling information Loss & coding: Content complexity visibility of errors, coding impact Problem: Transport Stream (MPEG2-TS) or Packetized Elementary Stream (PES) encryption no access to payload!

Approach Frame boundary detection (List et al., 2010)

GOP-detection (List et al, 2010)

Content complexity estimation from frames sizes (Garcia et al., 2012)

11


Content-complexityFrame size statistics (1)

Low complexity content (HD, 0.5Mbps)

12


Content-complexityFrame size statistics (2)

High complexity content (HD, 0.5Mbps)

13


content-specific weighting (per GOP) based on frame sizes Sn

Slicing parameter xwpSEQ xwpSEQsz content-sensitive

14

k

G

kkkk

T

TxlcxwpSEQsz 1

(Garcia et al. QoMEX 2011, Raake et al. 2011;Garcia 2012, unpublished;ITU-T Rec. P.1201.2)

N

kk

N

kkk

T

TxlxwpSEQ

1

1



=> Frame-based video quality modelSlicing – error-weighting based on content complexity

15(Garcia 2012, unpublished)

Qua

lity

Qua

lity

Error in GOPs not weightedError in GOPs weighted


R Video quality on T-V-Model scalePpl Packet loss percentagexwpSEQ Spatial-temporal measure of slicing degradationxwpSEQsz Spatial-temporal measure of slicing degradation, content-specific

with ck


Integral (audiovisual) qualityAudio-video quality-interaction

16

Video quality Audio quality

Audi

ovis

ual q

ualit

y

(Garcia et al., EURASIP, 2011)

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Deployment of media quality assessment modelsExample IPTV

17

IADTVSTB

TVM Probe

HDMI

Service Management System

T-V-Model probe

Probe versus model location

XY: X =location of measurement (N: Network, C: Client,

B: Both network and client)Y = location of model (N: Network, C: Client)

Options: CC current P.NAMS, P.MBAMS modelsCNNNBN



Thank you for your attention!

Visit www.aipa.tu-berlin.de for more information.


Quality-based

Audiovisual modeling approaches

19

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ItraVcItraAcIcodVcIcodAcQavoQav

vcatvtac

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visual)audioavvideo,vaudio,(a quality:Qx

loss frameor packet A)(X audioor V)(X videoofimpact quality : ncompressio A)(X audioor V)(X videoofimpact quality :

quality laudiovisua based:

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)( QvQaQvQaQav

Impairment factor-based

Addresses modality dominance & quality impact of degradation type

Addresses modality dominance (audio quality, video quality)

*(Allnatt, Wiley 1983)

(Overview see Garcia et al., 2011;Pinson, 2011)

(Garcia et al., EURASIP 2011)

Audio modality: Attention different in audio-only versus audiovisual case


Audio-video quality-interaction Degradation-type dependence

20

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coefficient name (Garcia et al., EURASIP 2011)

20

Audio modality: Attention different in audio-only versus audiovisual case

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