Recognition Tasks

  • Lucjan Janowski
  • Mikołaj Leszczuk
  • Mohamed-Chaker Larabi
  • Anna Ukhanova
Part of the T-Labs Series in Telecommunication Services book series (TLABS)


This chapter proposes a definition of Quality of Experience (QoE) in the case of task based applications. The definition is followed by the describing of the current work in the field of the QoE methodology in the specific case of a security system. Different metrics predicting QoE proposed in the literature are discussed.


Video Quality Subjective Test License Plate Security Application Psychophysical Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to these results has received funding from the European Communities Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 218086 (INDECT).


  1. 1.
    ITU-T Recommendation P.910 (1999) Subjective video quality assessment methods for multimedia applications. International Telecommunication Union, GenevaGoogle Scholar
  2. 2.
    ACLU (2011) Chicago’s video surveillance cameras. Technical report, ACLU of IllinoisGoogle Scholar
  3. 3.
    Möller S, Le Callet P, Perkis A (eds) (2012) Qualinet white paper on definitions of quality of experience: output version of the Dagstuhl seminar 12181, 1.1 edn. In: European network on quality of experience in multimedia systems and services (COST Action IC 1003), LausanneGoogle Scholar
  4. 4.
    ITU-T Recommendation BT.500-13 (2012) Methodology for the subjective assessment of the quality of television pictures. International Telecommunication Union, GenevaGoogle Scholar
  5. 5.
    Ford CG, McFarland MA, Stange IW (2009) Subjective video quality assessment methods for recognition tasks. In: Proceedings of the SPIE, vol 7240, pp 72, 400Z–72, 400Z–11Google Scholar
  6. 6.
    ITU-T Recommendation P.912 (2008) Subjective video quality assessment methods for recognition tasks. International Telecommunication Union, GenevaGoogle Scholar
  7. 7.
    Spangler T (2009) Golden eyes. Multichannel NewsGoogle Scholar
  8. 8.
    Leszczuk M, Koń A, Dumke J, Janowski L (2012) Redefining ITU-T P.912 recommendation requirements for subjects of quality assessments in recognition tasks. In: Dziech A, Czyewski A (eds) Multimedia communications, services and security, communications in computer and information science, vol 287. Springer, Heidelberg, pp 188–199Google Scholar
  9. 9.
    VQEG (2010) Report on the validation of video quality models for high definition video content, version 2.0 edn.
  10. 10.
    Janowski L (2012) Task-based subject validation: reliability metrics. In: Fourth international workshop on quality of multimedia experience (QoMEX), pp 182–187Google Scholar
  11. 11.
    Larabi MC, Nicholson D (2011) Monitoring image quality for security applications. In: IS&T/SPIE electronic imaging: image quality system performance. Burlingame, CAGoogle Scholar
  12. 12.
    Tsifouti A, Triantaphillidou S, Bilissi E, Larabi MC (2013) Acceptable nitrates for human face identification from CCTV imagery. In: IS&T/SPIE electronic imaging: image quality system performance. Burlingame, CAGoogle Scholar
  13. 13.
    Ghinea G, Chen SY (2008) Measuring quality of perception in distributed multimedia: verbalizers vs. imagers. Comput Hum Behav 24(4):1317–1329CrossRefGoogle Scholar
  14. 14.
    Ghinea G, Thomas JP (1998) QoS impact on user perception and understanding of multimedia video clips. In: Proceedings of the sixth ACM international conference on multimedia, MULTIMEDIA 98. ACM, New York, USA, pp 49–54. doi: 10.1145/290747.290754.
  15. 15.
    Janowski L, Romaniak P (2010) QoE as a function of frame rate and resolution changes. In: Zeadally S, Cerqueira E, Curado M, Leszczuk M (eds) Future multimedia networking. Lecture notes in computer science, vol 6157. Springer, Heidelberg, pp 34–45Google Scholar
  16. 16.
    Strohmeier D, Jumisko-Pyykko S, Kunze K (2010) Open profiling of quality: a mixed method approach to understanding multimodal quality perception. Adv Multiedia 3:1–3:17. doi: 10.1155/2010/658980.
  17. 17.
    Duplaga M, Leszczuk M, Papir Z, Przelaskowski A (2008) Evaluation of quality retaining diagnostic credibility for surgery video recordings. In: Proceedings of the 10th international conference on visual information systems: web-based visual information search and management, VISUAL’08, Springer, Heidelberg, pp 227–230Google Scholar
  18. 18.
    Nyman G, Radun J, Leisti T, Oja J, Ojanen H, Olives JL, Vuori T, Hakkinen J (2006) What do users really perceive—probing the subjective image quality experience. In: Proceedings of the SPIE international symposium on electronic imaging 2006: imaging quality and system performance III, vol 6059, pp 1–7Google Scholar
  19. 19.
    Radun J, Leisti T, Hakkinen J, Ojanen H, Olives Radun J, Leisti T, Hakkinen J, Ojanen H, Olives JL, Vuori T, Nyman G (2008) Content and quality: interpretation-based estimation of image quality. ACM Trans Appl Percept 4(2):1–2:15. doi: 10.1145/1278760.1278762.
  20. 20.
    Faye P, Bremaud D, Daubin MD, Courcoux P, Giboreau A, Nicod H (2004) Perceptive free sorting and verbalisation tasks with naive subjects: an alternative to descriptive mappings. Food Qual Prefer 15(7–8):781–791. doi: 10.1016/j.foodqual.2004.04.009. (Fifth Rose Marie Pangborn Sensory Science Symposium)
  21. 21.
    Picard D, Dacremont C, Valentin D, Giboreau A (2003) Perceptual dimensions of tactile textures. Acta Psychol 114(2):165–184. doi: 10.1016/j.actpsy.2003.08.001. Google Scholar
  22. 22.
    Leszczuk M, Janowski L, Romaniak P, Glowacz A, Mirek R (2011) Quality assessment for a licence plate recognition task based on a video streamed in limited networking conditions. In: Dziech A, Czyewski A (eds) Multimedia communications, services and security, communications in computer and information science, vol 149. Springer, Heidelberg, pp 10–18Google Scholar
  23. 23.
    Janowski L, Kozlowski P, Baran R, Romaniak P, Glowacz A, Rusc T (2012) Quality assessment for a visual and automatic license plate recognition. Multimedia Tools Appl 1–18. doi: 10.1007/s11042-012-1199-5
  24. 24.
    Dumke J (2013) Visual acuity and task-based video quality in public safety applications. In: Proceedings of the SPIE 8653, image quality and system performance X, 865306 pp 865, 306–865, 306–307. doi: 10.1117/12.2004882.
  25. 25.
    Leszczuk M (2011) Assessing task-based video quality—a journey from subjective psycho-physical experiments to objective quality models. In: Dziech A, Czyewski A (eds) Multimedia communications, services and security, communications in computer and information science, vol 149. Springer, Heidelberg, pp 91–99Google Scholar
  26. 26.
    Leszczuk M (2012) Optimising task-based video quality. Multimedia Tools Appl 1–18. doi: 10.1007/s11042-012-1161-6
  27. 27.
    Maalouf A, Larabi MC, Nicholson D (2012) Offline quality monitoring for legal evidence images in video-surveillance applications. Multimedia Tools Appl 1–30. doi: 10.1007/s11042-17012-17126817-9
  28. 28.
    Leszczuk MI, Stange I, Ford C (2011) Determining image quality requirements for recognition tasks in generalized public safety video applications: definitions, testing, standardization, and current trends. In: IEEE International symposium on the broadband multimedia systems and broadcasting (BMSB), pp 1–5. doi: 10.1109/BMSB.2011.5954938
  29. 29.
    VQEG (2013) The video quality experts group.
  30. 30.
    Leszczuk M, Dumke J (2012) The quality assessment for recognition tasks (QART), VQEG.
  31. 31.
    ISO-22311:2012 (2012) Societal security videosurveillance format for interoperability. Technical report, ISO 2012Google Scholar
  32. 32.
    Keimel C, Habigt J, Horch C, Diepold K (2012) Video quality evaluation in the cloud. In: Proceddings of 19th international packet video workshop (PV) 2012, pp 155–160Google Scholar
  33. 33.
    Kunka B, Kostek B (2009) Non-intrusive infrared-free eye tracking method. In: Signal processing algorithms, architectures, arrangements, and applications conference proceedings (SPA) 2009, pp 105–109Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lucjan Janowski
    • 1
  • Mikołaj Leszczuk
    • 1
  • Mohamed-Chaker Larabi
    • 2
  • Anna Ukhanova
    • 3
  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.XLIMUniversité de PoitiersPoitiersFrance
  3. 3.Technical University of DenmarkKongens LyngbyDenmark

Personalised recommendations