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Journal of Network and Systems Management

Erschienen in:

01.01.2016

Assessing the QoE in Video Services Over Lossy Networks

verfasst von: Francisco J. Suárez, Andrés García, Juan C. Granda, Daniel F. García, Pelayo Nuño

Erschienen in: Journal of Network and Systems Management | Ausgabe 1/2016

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Abstract

Quality of experience is of critical importance in streaming video services, because the traditional quality of service cannot represent the quality perceived by viewers. This work evaluates several objective quality metrics under realistic bursty packet loss conditions in the network, with the support of a packet loss model. Alignment of reference and streamed video sequences (with different levels of spatial-temporal information) are also explored as a technique to prevent inaccurate computation of objective metrics due to frame loss. Finally, the correlation between subjective and objective metrics for each motion level and the computing time of metrics are analysed. The most suitable objective metrics to characterize the real degradation in the quality perceived by viewers, for both off-line and real-time assessment, are proposed. The integration of motion, busty packet loss, sequence alignment after frame loss and computing time of metrics are the main contributions of this research work.

Vorheriger Artikel Phenomena Detection in Mobile Wireless Sensor Networks

Nächster Artikel JRCAP: A Joint Routing and Channel Assignment Protocol for Multi-radio Multi-channel IEEE 802.11s Mesh Networks

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Altman, E., Avrachenkov, K., Barakat, C.: TCP in presence of bursty losses. Perform. Eval. 42(2–3), 129–147 (2000). doi:10.1016/S0166-5316(00)00027-4 CrossRefMATH

Hasslinger, G., Hohlfeld, O.: The Gilbert–Elliott model for packet loss in real time services on the internet. In: Proceedings of the 14th GI/ITG Conference on Measuring. Modelling and Evaluation of Computer and Communication Systems (MMB 2008), pp. 269–283. Dortmund, Germany (2008)

Ellis, M., Pezaros, D., Kypraios, T., Perkins, C.: Modelling packet loss in RTP-based streaming video for residential users. In: Proceedings of the 37th IEEE Conference on Local Computer Networks (LCN 2012), pp. 220–223. Clearwater, USA (2012). doi:10.1109/LCN.2012.6423613

Van Moorsel, A.: Metrics for the internet age: quality of experience and quality of business. In: Proceedings of the 5th Performability Workshop, pp. 26–31. Erlangen, Germany (2001)

Chikkerur, S., Sundaram, V., Reisslein, M., Karam, : Objective video quality assessment methods: a classification, review, and performance comparison. IEEE Trans. Broadcast. 57(2), 165–182 (2011). doi:10.1109/TBC.2011.2104671 CrossRef

Claypool, M., Tanner, J.: The effects of jitter on the peceptual quality of video. In: Proceedings of the 7th ACM International Conference on Multimedia (ACM Multimedia’99) (part 2), pp. 115–118. Orlando, USA (1999). doi:10.1145/319878.319909

Boyce, J., Gaglianello, R.: Packet loss effects on MPEG video sent over the public internet. In: Proceedings of the 6th ACM International Conference on Multimedia (ACM Multimedia’98), pp. 181–190. Bristol, England (1998). doi:10.1145/290747.290770

Greengrass, J., Evans, J., Begen, A.: Not all packets are equal, part 2: the impact of network packet loss on video quality. IEEE Internet Comput. 13(2), 74–82 (2009). doi:10.1109/MIC.2009.40 CrossRef

Boyaci, O., Forte, A., Schulzrinne, H.: Performance of video-chat applications under congestion. In: Proceedings of the 11th IEEE International Symposium on Multimedia (ISM’09), pp. 213–218. San Diego, USA (2009). doi:10.1109/ISM.2009.45

10.

Pinson, M., Wolf, S., Cermak, G.: HDTV subjective quality of H.264 vs. MPEG-2, with and without packet loss. IEEE Trans. Broadcast. 56(1), 86–91 (2010). doi:10.1109/TBC.2009.2034511 CrossRef

11.

Loguinov, D., Radha, H.: Large-scale experimental study of Internet performance using video traffic. Comput. Commun. Rev. 32(1), 7–19 (2002). doi:10.1145/510726.510727 CrossRef

12.

Calyam, P., Lee, C.G.: Characterizing voice and video traffic behavior over the Internet. In: Proceedings of the 20th International Symposium on Computer and Information Sciences (ISCIS 2005). Istanbul, Turkey (2005)

13.

Ellis, M., Perkins, C.: Packet loss characteristics of IPTV-like traffic on residential links. In: Proceedings of the 7th Annual IEEE Consumer Communications and Networking Conference (CCNC 2010), pp. 1–5. Las Vegas, USA (2010). doi:10.1109/CCNC.2010.5421735

14.

Final reports from the video quality experts group on the validation of objective models of video and multimedia quality assessment. Technical report, Video Quality Experts Group (VQEG). “http://www.its.bldrdoc.gov/vqeg/reports.aspx”. Accessed 23 Jan 2015

15.

Besson, A., De Simone, F., Ebrahimi, T.: Objective quality metrics for video scalability. In: Proceedings of the 20th IEEE International Conference on Image Processing (ICIP), pp. 59–63. Melbourne, Australia (2013). doi: 10.1109/ICIP.2013.6738013

16.

Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). doi:10.1109/TIP.2003.819861 CrossRef

17.

Wang, Z., Simoncelli, E., Bovik, A.: Multi-scale structural similarity for image quality assessment. In: Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402. Piscataway, USA (2003). doi:10.1109/ACSSC.2003.1292216

18.

Damera-Venkata, N., Kite, T., Geisler, W., Evans, B., Bovik, A.: Image quality assessment based on a degradation model. IEEE Trans. Image Process. 9(4), 636–650 (2000). doi:10.1109/83.841940 CrossRef

19.

Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002). doi:10.1109/97.995823 CrossRef

20.

Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006). doi:10.1109/TIP.2005.859378 CrossRef

21.

Rimac-Drlje, S., Vranjes, M., Zagar, D.: Influence of temporal pooling method on the objective video quality evaluation. In: Proceedings of the 2nd IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB ’09), pp. 1–5. Bilbao, Spain (2009). doi:10.1109/ISBMSB.2009.5133781

22.

Seufert, M., Slanina, M., Egger, S., Kottkamp, M.: “To pool or not to pool”: A comparison of temporal pooling methods for HTTP adaptive video streaming. In: Proceedings of the 5th International Workshop on Quality of Multimedia Experience (QoMEX 2013), pp. 52–57. Klagenfurt, Austria (2013). doi:10.1109/QoMEX.2013.6603210

23.

Wang, Z., Lu, L., Bovik, A.: Video quality assessment based on structural distortion measurement. Signal Process. Image 19(2), 121–132 (2004). doi:10.1016/S0923-5965(03)00076-6 CrossRef

24.

Sheikh, H., Bovik, A.: A visual information fidelity approach to video quality assessment. In: Proceedings of the 1st International Workshop on Video Processing and Quality Metrics for Consumer Electronics, pp. 23–25. Scottsdale, USA (2005)

25.

Politis, I., Dounis, L., Tselios, C., Kordelas, A., Dagiuklas, T., Papadakis, A.: A model of network related QoE for 3D video. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM 2012), pp. 1335–1340. Anaheim, USA (2012). doi: 10.1109/GLOCOMW.2012.6477776

26.

Xiao, F.: DCT-based video quality evaluation. Technical Report, MSU Graphics and Media Lab (Video Group) (2000). “http://compression.ru/video/quality_measure/vqm”. Accessed 23 Jan 2015

27.

Watson, A., Hu, J., McGowan III, J.: DVQ: a digital video quality metric based on human vision. J. Electron. Imaging 10, 20–29 (2001). doi:10.1117/1.1329896 CrossRef

28.

Pinson, M., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50(3), 312–322 (2004). doi:10.1109/TBC.2004.834028 CrossRef

29.

Seshadrinathan, K., Bovik, A.: Motion tuned spatio-temporal quality assessment of natural videos. IEEE Trans. Image Process. 19(2), 335–350 (2010). doi:10.1109/TIP.2009.2034992 MathSciNetCrossRef

30.

MPEVQ, Advanced perceptual evaluation of video quality. Technical Report, OPTICOM GmbH (2005). “http://www.opticom.de/download/PEVQ-WP-v07-A4”. Accessed 23 Jan 2015

31.

Soundararajan, R., Bovik, A.: Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Trans. Circuits Syst. Video Technol. 23(4), 684–694 (2013). doi:10.1109/TCSVT.2012.2214933 CrossRef

32.

Narwaria, M., Lin, W., Liu, A.: Low-complexity video quality assessment using temporal quality variations. IEEE Trans. Multimed. 14(3–1), 525–535 (2012). doi:10.1109/TMM.2012.2190589 CrossRef

33.

Yang, F., Song, J., Wan, S., Wu, H.: Content-adaptive packet-layer model for quality assessment of networked video services. IEEE J. Sel. Top. Signal Process. 6(6), 672–683 (2012). doi:10.1109/JSTSP.2012.2207705 CrossRef

34.

Joskowicz, J., Sotelo, R., López-Ardao, J.: Towards a general parametric model for perceptual video quality estimation. IEEE Trans. Broadcast. 59(4), 569–579 (2013). doi:10.1109/TBC.2013.2277951 CrossRef

35.

Mohamed, S., Rubino, G.: A study of real-time packet video quality using random neural networks. IEEE Trans. Circuits Syst. Video Technol. 12(12), 1071–1083 (2002). doi:10.1109/TCSVT.2002.806808 CrossRef

36.

Khan, A., Sun, L., Ifeachor, E., Fajardo, J., Liberal, F.: Impact of RLC losses on quality prediction for H.264 video over UMTS networks. In: Proceedings of the 11th IEEE International Conference on Multimedia and Expo (ICME), pp. 702–707. Singapore (2010). doi: 10.1109/ICME.2010.5583033

37.

Mok, R., Chan, E., Chang, R.: Measuring the quality of experience of HTTP video streaming. In: Proceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011), pp. 485–492. Dublin, Ireland (2011). doi: 10.1109/INM.2011.5990550

38.

Reibman, A., Vaishampayan, V., Sermadevi, Y.: Quality monitoring of video over a packet network. IEEE Trans. Multimed. 6(2), 327–334 (2004). doi:10.1109/TMM.2003.822785 CrossRef

39.

Babu, R., Bopardikar, A., Perkis, A., Hillestad, O.: No-reference metrics for video streaming applications. In: Proceedings of the 14th International Workshop on Packet Video (PV 2004). Irvine, USA (2004)

40.

Venkataraman, M., Chatterjee, M., Chattopadhyay, S.: Evaluating quality of experience for streaming video in real time. In: Proceedings of the 24th IEEE Global Communications Conference (GLOBECOM 2009), pp. 1–6. Honolulu, USA (2009). doi:10.1109/GLOCOM.2009.5425806

41.

Laghari, K., Pham, T., Nguyen, H., Crespi, N.: QoM: A new quality of experience framework for multimedia services. In: Proceedings of the 17th IEEE Symposium on Computers and Communications (ISCC 2012), pp. 851–856. Cappadocia, Turkey (2012). doi: 10.1109/ISCC.2012.6249408

42.

Staelens, N., Van Wallendael, G., Crombecq, K., Vercammen, N., De Cock, J., Vermeulen, B., Van de Walle, R., Dhaene, T., Demeester, P.: No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences. IEEE Trans. Broadcast. 58(2), 187–199 (2012). doi:10.1109/TBC.2012.2189334 CrossRef

43.

Chan, A., Zeng, K., Mohapatra, P., Lee, S., Banerjee, S.: Metrics for evaluating video streaming quality in lossy IEEE 802.11 wireless networks. In: Proceedings of the 29th IEEE International Conference on Computer Communications (INFOCOM 2010), pp. 1613–1621. San Diego, USA (2010). doi:10.1109/INFCOM.2010.5461979

44.

McDonagh, P., Pande, A., Murphy, L., Mohapatra, P.: Toward deployable methods for assessment of quality for scalable IPTV services. IEEE Trans. Broadcast. 59(2), 223–237 (2013). doi:10.1109/TBC.2013.2255776 CrossRef

45.

Subjective video quality assessment methods for multimedia applications (ITU-T P.910 Recommendation). Technical Report, International Telecommunication Union (ITU) (2009). “http://www.itu.int/rec/T-REC-P.910-200804-I”. Accessed 23 Jan 2015

46.

Gilbert, E.: Capacity of a burst-noise channel. Bell Syst. Tech. J. 39(5), 1253–1265 (1960). doi:10.1002/j.1538-7305.1960.tb03959.x CrossRef

47.

Elliott, E.: Estimates of error rates for codes on burst-noise channels. Bell Syst. Tech. J. 42(5), 1977–1997 (1963). doi:10.1002/j.1538-7305.1963.tb00955.x CrossRef

48.

Salsano, S., Ludovici, F., Ordine, A., Giannuzzi, D.: Definition of a general and intuitive loss model for packet networks and its implementation in the Netem module in the Linux kernel. Technical Report, University of Rome (2012). “http://netgroup.uniroma2.it/TR/TR-loss-netem”. Accessed 23 Jan 2015

49.

Reference algorithm for computing peak signal to noise ratio of a processed video sequence with compensation for constant spatial shifts, constant temporal shift, and constant luminance gain and offset (ITU-T J.340 Recommendation). Technical Report, International Telecommunication Union (ITU) (2010). “https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-J.340-201006-I!!PDF-E&type=items”. Accessed 23 Jan 2015

50.

VLC media player. “http://www.videolan.org/vlc/”. VideoLAN Organization. Accessed 23 Jan 2015

51.

García, P.: Python visual quality metric package (PyMetrikz). “https://bitbucket.org/kuraiev/pymetrikz”. University of Brasilia. Accessed 23 Jan 2015

52.

Moskow State University (MSU) Video quality measurement tool. “http://compression.ru/video/quality_measure/video_measurement_tool_en.html”. MSU Graphics and Media Lab (Video Group). Accessed 23 Jan 2015

53.

Visual quality metric (VQM). “http://www.its.bldrdoc.gov/resources/video-quality-research/software.aspx”. Institute of Telecommunication Sciences (ITS), US National Telecommunication and Information Administration (NTIA). Accessed 23 Jan 2015

54.

Test video sequences. “http://www.atc.uniovi.es/personal/fran/papers/SuarezFJ13-videos.rar”. Computer Engineering Group at University of Oviedo. Accessed 23 Jan 2015

Titel: Assessing the QoE in Video Services Over Lossy Networks
verfasst von: Francisco J. Suárez
Andrés García
Juan C. Granda
Daniel F. García
Pelayo Nuño
Publikationsdatum: 01.01.2016
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 1/2016
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-015-9343-y

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