Standardization activities for non-intrusive quality monitoring of multimedia services

Transcription

1 Standardization activities for non-intrusive quality monitoring of multimedia services Alexander Raae, Marie-Neige Garcia, Savvas Argyropoulos, Michal Soloducha Peter List, Bernhard Feiten, TU Berlin, Germany ETSI STQ Worshop, Vienna, Nov Drawing: Sandra Buchmüller

2 Standardized models Bitstream/parametric 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) Source signal (SRC) Transmission- System Subjective qualityrating Bitstream / Parameters Diagnostic information Model Estimated quality index 2

3 Teleom-Video-model (T--V-model): Multi-layer framewor Models ITU-T SG12 model submission: Dec P.NAMS (audiovisual) P.NBAMS (video-only) Consent on P.NAMS and P.NBAMS: Sept. 7, 2012 (Raae et al., IEEE SPM 2011) 3

4 Monitoring model standardization activities ITU-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 Pacet-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 Standards consented 7 Sept

5 Controlled assessment procedure P.NAMS, P.NBAMS: End-to-end video transmission chain COPYRIGHT TELEKOM INNOVATION LABORATORIES 5

6 Controlled assessment procedure Example P.NAMS, P.NBAMS: Subjective test databases Training 15 (official) training databases Validation 24 validation databases Audio Video Audiovisual LR HR Audio Video Audiovisual LR HR

7 Example P P.NAMS-HR encrypted streams (Under study) Models P P P (Under study) T-labs/DTAG wor see: Argyropoulos et al , Garcia et al , Raae et al

8 Parametric video quality model T-V-model Base model Qv Qvo Icod Itra Impairment factors: Quality-related counterpart of technical degradations Additive on perceptual quality rating scale No loss: Icod a 1 exp( a2 bpp) a 3 S I a 4 Qvo: best possible quality S I : transformed I-frame size bpp : bits per pixel 6 bitrate 10 bpp resolution framerate Loss, PLC = freezing: Itra b0 log( b1 dpseq bpp 1) dpseq: total freezing xwpseqsz Loss, PLC = slicing: Itra ( c0 Icod) log( c1 1) Icod xwpseqsz: spatio-temporal slicing degradation (Garcia & Raae, QoMEX 2011; ITU-T Rec. P ) 8

9 Slicing parameter xwpseq xwpseqsz: magnitude (spatial extent and duration) of loss degradation xl N xli ( T ti ) i 1 T xwpseq N xl 1 N xl Loss degradation density (per GOP ) xl i Initial loss degradation of frame i Note: corrected for slice-size! t i Time of loss event vs. GOP-start Duration GOP T 1 T T (Garcia & Raae, QoMEX 2011; Raae et al. 2011; ITU-T Rec. P ) COPYRIGHT TELEKOM INNOVATION LABORATORIES 9

10 Slicing parameter xwpseq Still a problem: content-dependency Quality Quality R Ppl xwpseq Video quality on T-V-Model scale Pacet loss percentage Spatial-temporal measure of slicing degradation (no content-correction) (Garcia & Raae, QoMEX 2011) COPYRIGHT TELEKOM INNOVATION LABORATORIES 10 (Garcia 2012, unpublished) 10

11 Content-complexity & error-propagation Inference from GOP-analysis Desired modelling information Loss & coding: Content complexity visibility of errors, coding impact Problem: Transport Stream (MPEG2-TS) or Pacetized 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

12 Content-complexity Frame size statistics (1) Low complexity content (HD, 0.5Mbps) 12

13 Content-complexity Frame size statistics (2) High complexity content (HD, 0.5Mbps) 13

14 Slicing parameter xwpseq xwpseqsz content-sensitive xwpseq N xl 1 N 1 T T G 1 xwpseqsz c xl T T content-specific weighting (per GOP) based on frame sizes S n (Garcia et al. QoMEX 2011, Raae et al. 2011; Garcia 2012, unpublished; ITU-T Rec. P ) COPYRIGHT TELEKOM INNOVATION LABORATORIES 14

15 => Frame-based video quality model Slicing error-weighting based on content complexity Error in GOPs weighted Error in GOPs not weighted with c Quality Quality R Video quality on T-V-Model scale Ppl Pacet loss percentage xwpseq Spatial-temporal measure of slicing degradation xwpseqsz Spatial-temporal measure of slicing degradation, content-specific COPYRIGHT TELEKOM INNOVATION LABORATORIES 15 (Garcia 2012, unpublished) 15

16 Integral (audiovisual) quality Audio-video quality-interaction Audiovisual quality Video quality Audio quality (Garcia et al., EURASIP, 2011) COPYRIGHT TELEKOM INNOVATION LABORATORIES 16 16

17 Deployment of media quality assessment models Example IPTV T-V-Model probe Probe versus model location HDMI TV STB TVM Probe IAD XY: X =location of measurement (N: Networ, C: Client, B: Both networ and client) Y = location of model (N: Networ, C: Client) Service Management System Options: CC current P.NAMS, P.MBAMS models CN NN BN COPYRIGHT TELEKOM INNOVATION LABORATORIES 17

18 Than you for your attention! Visit for more information. 18

19 Audiovisual modeling approaches Quality-based Qav Qa Qv ( Qa Qv) Qx : quality (a audio, v video, av audio visual) Addresses modality dominance (audio quality, video quality) Audio modality: Attention different in audio-only versus audiovisual case (Overview see Garcia et al., 2011; Pinson, 2011) Impairment factor-based Qav Qavo c Qavo: c c ac, vc ac, vt Addresses modality dominance & quality impact of degradation type ac IcodA c IcodA IcodV c IcodA ItraV c vc based audiovisual quality IcodV c at, vt at, vc at ItraA c ItraA ItraV ItraA IcodV Icodx:quality impact of video(x V) or audio (X A) compression vt ItraV Icodx:quality impact of video(x V) or audio (X A) pacet or frameloss (Garcia et al., EURASIP 2011) *(Allnatt, Wiley 1983) 19

20 Audio-video quality-interaction Degradation-type dependence Qav Qavo c c c ac, vc ac, vt ac IcodA c vc IcodA IcodV c IcodA ItraV c IcodV c at, vt at, vc at ItraA c ItraA ItraV ItraA IcodV vt ItraV model coefficient value Audio modality: Attention different in audio-only versus audiovisual case coefficient name (Garcia et al., EURASIP 2011) 20 20