Advertisement

Quality of Experience and Quality of Service Metrics for 3D Content

  • Miguel Barreda-ÁngelesEmail author
  • Federica Battisti
  • Giulia Boato
  • Marco Carli
  • Emil Dumic
  • Margrit Gelautz
  • Chaminda Hewage
  • Dragan Kukolj
  • Patrick Le-Callet
  • Antonio Liotta
  • Cecilia Pasquini
  • Alexandre Pereda-Baños
  • Christos Politis
  • Dragana Sandic
  • Murat Tekalp
  • María Torres-Vega
  • Vladimir Zlokolica
Chapter
First Online:
  • 827 Downloads
Part of the Signals and Communication Technology book series (SCT)

Abstract

Traditionally, the quality of a multimedia system was mainly assessed through the evaluation of its Quality of Service (QoS) that is by evaluating system parameters such as bandwidth, latency, jitter, throughput, transmission delay, availability, etc. However, these metrics often failed to capture the actual end-user perceived quality, which has prompted the development of the construct of Quality of Experience (QoE), widely understood as an interaction of the technical features of multimedia systems with perceptual, and cognitive/emotional factors involved in the interpretation of those features by users. This chapter addresses the open issues in the field of QoS and QoE assessments. First, the perceptual characteristics of the multiview content are analyzed, and then a survey on the existing approaches for QoS and QoE estimation is performed. The analysis is then focused on the subjective aspects of QoE assessment, by describing the standard methodologies currently used and new trends based on human factors research. Finally, the chapter offers a few guidelines for future research directions in the field.

Keywords

Peak Signal-to-noise Ratio (PSNR) Depth-image-based Rendering (DIBR) Stereoscopic Content Depth Map Synthesis Artifacts 
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.
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Liotta, A.: The cognitive NET is coming. IEEE Spectr 50(8), 26–31 (2013)Google Scholar
  2. 2.
    Torres Vega, M., Zou, S., Mocanu, D.C., Tangdiongga, E., Koonen, A.M.J., Liotta, A.: End-to-end performance evaluation in high-speed wireless networks. In: Proceedings of the 2014 10th International Conference on Network and Service Management (CNSM), 17–21 Nov 2014, Rio de Janeiro, Brazil (pp. 344–347). Piscataway: IEEE Service Center (2014)Google Scholar
  3. 3.
    Le Callet, P., Moeller, S., Perkins, A. (eds.) Qualinet white paper on definitions of quality of experience. In: COST Action IC 1003, Laussane, Switzerland (2012)Google Scholar
  4. 4.
    Agboma, F., Liotta, A.: Quality of experience management in mobile content delivery systems. J. Telecommun. Syst. (special issue on the Quality of Experience issues in Multimedia Provision) 49(1) (2012)Google Scholar
  5. 5.
    Agboma, F., Liotta, A.: QoE-aware QoS management. In: Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia, 24–26 Nov 2008, Linz (Austria)Google Scholar
  6. 6.
    Mocanu, D.C., Liotta, A., Ricci, A., Torres Vega, M., Exarchakos, G.: When does lower bitrate give higher quality in modern video services? In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS’14) 5–9 May 2014, Krakow, Poland, IEEE, Piscataway, pp. 1–5 (2014Google Scholar
  7. 7.
    Agboma, F., Liotta, A.: Addressing user expectations in mobile content delivery. Mobile Inf. Syst. 3(3–4), 153–164 (2007)CrossRefGoogle Scholar
  8. 8.
    Agboma, F., Smy, M., Liotta, A. “QoE analysis of a peer-to-peer television systems”. In: Proceedings of IADIS International Conference on Telecommunications, Networks and Systems. July 2008Google Scholar
  9. 9.
    Agboma, F., Liotta, A.: Quality of experience management in mobile content delivery systems. Telecommun. Syst. (2012)Google Scholar
  10. 10.
    Exarchakos, G., Druda, L., Menkovski, V., Liotta, A.: Network analysis on Skype end-to-end video quality. Int. J. Pervasive Comput. Commun. 11(1) (2015)Google Scholar
  11. 11.
    Exarchakos, G., Menkovski, V., Liotta, A.: Can Skype be used beyond video calling?. In: Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia (MOMM11), ACM, 2011/12/5Google Scholar
  12. 12.
    Liotta, A., Druda, L., Menkovski, V., Exarchakos, G.: Quality of experience management for video streams: the case of Skype. In: Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia, ACM, 2012/12/3Google Scholar
  13. 13.
    Exarchakos, G., Druda, L., Menkovski, V., Bellavista, P., Liotta, A.: Skype resilience to high motion videos. Int. J. Wavelets Multiresolution Inf. Process 11(03) (2013)Google Scholar
  14. 14.
    Menkovski, V., Exarchakos, G., Liotta, A., Cuadra Sánchez, A.: Measuring quality of experience on a commercial mobile TV platform. In: 2010 Second International Conferences on Advances in Multimedia (MMEDIA), Athens (Greece), IEEE, 13–19 June 2010Google Scholar
  15. 15.
    Menkovski, V., Exarchakos, G., Liotta, A.: Adaptive testing for video quality assessment. In: Proceedings of Quality of Experience for Multimedia Content Sharing, Lisbon, Portugal, 29 June 2011Google Scholar
  16. 16.
    Torres Vega, M., Sguazzo, V., Mocanu, D.C., Liotta, A.: An experimental survey of no-reference video quality assessment methods. Int. J. Pervasive Comput. Commun. 12(1) (2016)Google Scholar
  17. 17.
    Liotta, A., Mocanu, D.C., Menkovski, V., Cagnetta, L., Exarchakos, G.: Instantaneous video quality assessment for lightweight devices. In: Proceedings of International Conference on Advances in Mobile Computing & Multimedia, ACM, 2013/12/2Google Scholar
  18. 18.
    Mocanu, D.C., Santandrea, G., Cerroni, W., Callegati, F., Liotta, A.: Network performance assessment with quality of experience benchmarks. In: 2014 10th International Conference on Network and Service Management (CNSM), IEEE, 17 Nov 2014Google Scholar
  19. 19.
    Winkler, S., Mohandas, P.: The evolution of video quality measurement: from PSNR to hybrid metrics. IEEE Trans. Broadcast. 54(3), 660–668 (2008)CrossRefGoogle Scholar
  20. 20.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  21. 21.
    Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50(3), 312–322 (2004)Google Scholar
  22. 22.
    Chikkerur, S., Sundaram, V., Reisslein, M., Karam, L.J.: Objective video quality assessment methods: a classification, review, and performance comparison. TBC 57(2), 165–182 (2011)Google Scholar
  23. 23.
    Torres Vega, M., Sguazzo, V., Mocanu, D.C., Liotta, A.: Accuracy of no-reference quality metrics in network-impaired video streams. In: 13th International Conference on Advances in Mobile Computing and Multimedia, 11–13 Dec 2015, ACM, Brussels, Belgium BrusselsGoogle Scholar
  24. 24.
  25. 25.
    Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: Study of subjective and objective quality assessment of video. Trans. Image Process. 19(6), 1427–1441 (2010)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. The MIT Press, Cambridge (2012)Google Scholar
  27. 27.
    Menkovski, V., Oredope, A., Liotta, A., Cuadra-Sanchez, A.: Optimized online learning for QoE prediction. In: Proceedings of the 21st Benelux Conference on Artificial Intelligence, 2009/10/9Google Scholar
  28. 28.
    Menkovski, V., Exarchakos, G., Liotta, A.: Online QoE prediction. Second International Workshop on Quality of Multimedia Experience (QoMEX), IEEE, June 2010Google Scholar
  29. 29.
    Menkovski, V., Exarchakos, G., Liotta, A., Cuadra Sánchez, A.: Estimations and remedies for quality of experience in multimedia streaming. In: Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services (CENTRIC), IEEE, 2010/8/22Google Scholar
  30. 30.
    Menkovski, V., Exarchakos, G., Liotta, A.: Online learning for quality of experience management. In: The Annual Machine Learning Conference of Belgium and the Netherlands (2010)Google Scholar
  31. 31.
    Menkovski, V., Exarchakos, G., Liotta, A.: Machine learning approach for quality of experience aware networks. In: 2nd International Conference on Intelligent Networking and Collaborative Systems (INCOS), IEEE, 2010/11/24Google Scholar
  32. 32.
    Menkovski, V., Exarchakos, G., Liotta, A.: The value of relative quality in video delivery. J. Mobile Multimed. 7(3), 151–162 (2011)Google Scholar
  33. 33.
    Menkovski, V., Exarchakos, G., Liotta, A., Cuadra Sánchez, A.: Quality of experience models for multimedia streaming. In: Advancing the Next-Generation of Mobile Computing: Emerging Technologies: Emerging Technologies. IGI Global (2012)Google Scholar
  34. 34.
    Menkovski, V., Exarchakos, G., Liotta, A.: Tackling the sheer scale of subjective QoE. In: International ICST Mobile Multimedia Communications Conference, vol. 29. Springer, Berlin (2012)Google Scholar
  35. 35.
    Menkovski, V., Liotta, A.: Adaptive psychometric scaling for video quality assessment. Signal Process. Image Commun. 27(8) (2012)Google Scholar
  36. 36.
    Bertone, F., Menkovski, V., Liotta, A.: Adaptive P2P streaming. In: Streaming Media with Peer-to-Peer Networks. IGI Global (2012)Google Scholar
  37. 37.
    Menkovski, V., Liotta, A.: Intelligent control for adaptive video streaming. In: Proceedings of the International Conference on Consumer Electronics, Jan 2013Google Scholar
  38. 38.
    Torres Vega, M., Mocanu, D.C., Barresi, R., Fortino, G., Liotta, A.: Cognitive streaming on android devices. In: Proceedings of the IEEE/IFIP Symposium on Integrated Network and Service Management (IM’15), 11–15 May 2015, Ottawa, Canada, pp. 1316–1321. IEEE Service Center, Piscataway (2015)Google Scholar
  39. 39.
    Torres Vega, M., Giordano, E., Mocanu, D.C., Tjondronegoro, D., Liotta, A.: Cognitive no-reference video quality assessment for mobile streaming services. In: Proceedings of the 7th International Workshop on Quality of Multimedia Experience (QoMex), 26–29 May 2015, Pilos, Messinia, Greece, pp. 1–6. IEEE Service Center, PiscatawayGoogle Scholar
  40. 40.
    Mocanu, D.C., Exarchakos, G., Bou Ammar, H., Liotta, A.: Reduced reference image quality assessment via Boltzmann machines. In: IFIP/IEEE International Symposium on Integrated Network Management (IM), 11 May 2015. IEEE (2015)Google Scholar
  41. 41.
    Mocanu, D.C., Exarchakos, G., Liotta, A.: Deep learning for objective quality assessment of 3D images. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 27 Oct 2014Google Scholar
  42. 42.
    ITU-R BT.2021, Subjective methods for the assessment of stereoscopic 3DTV systems (2012)Google Scholar
  43. 43.
    Winkler, S., Min, D.: Stereo/multiview picture quality: overview and recent advances. Sig. Process. Image Commun. 28(10), 1358–1373 (2013)CrossRefGoogle Scholar
  44. 44.
    Zhu, C., Li, S.: Depth image based view synthesis: new insights and perspectives on hole generation and filling. IEEE Trans. Broadcast. 62(1), 82–93 (2016)CrossRefGoogle Scholar
  45. 45.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002)CrossRefGoogle Scholar
  46. 46.
    Liu, W., Chen, X., Yang, J., Wu, Q.: Robust color guided depth map restoration. IEEE Trans. Image Process. 26(1), 315–327 (2017)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Lei, J., Li, L., Yue, H., Wu, F., Ling, N., Hou, C.: Depth map super-resolution considering view synthesis quality. IEEE Trans. Image Process. 26(4), 1732–1745 (2017)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Nezveda, M., Brosch, N., Seitner, F., Gelautz, M.: Depth map post-processing for depth-image-based rendering: a user study. In: Proceedings of SPIE 9011, Stereoscopic Displays and Applications XXV, 90110K (2014).  https://doi.org/10.1117/12.2039771
  49. 49.
    Bosc, E., Pepion, R., Le Callet, P., Pressigout, M., Morin, L.: Reliability of 2D quality assessment methods for synthesized views evaluation in stereoscopic viewing conditions. In: Proceedings of 3DTV-Con, pp. 1–4 (2012).  https://doi.org/10.1109/3dtv.2012.6365457
  50. 50.
    Seitner, F., Nezveda, M., Gelautz, M., Braun, G., Kapeller, C., Zellinger, W., Moser, B.: Trifocal system for high-quality inter-camera mapping and virtual view synthesis. In: Proceedings of International Conference on 3D Imaging (IC3D), 1–8 (2015).  https://doi.org/10.1109/ic3d.2015.7391819
  51. 51.
    Muddala, S., Olsson, R., Sjöström, M.: Spatio-temporal consistent depth-image-based rendering using layered depth image and inpainting. EURASIP J. Image Video Process. 9(1), 1–19 (2016)Google Scholar
  52. 52.
    Buyssens, P., Le Meur, O., Daisy, M., Tschumperlé, D., Lézoray, O.: Depth-guided disocclusion inpainting of synthesized RGB-D images. IEEE Trans. Image Process. 26(2), 525–538 (2017)MathSciNetCrossRefGoogle Scholar
  53. 53.
    Bosc, E., Pépion, R., Le Callet, P., Köppel, M., Ndjiki-Nya, P., Pressigout, M., Morin, L.: Towards a new quality metric for 3-D synthesized view assessment. IEEE J. Sel. Top. Sign. Proces. 5(7), 1332–1343 (2011)CrossRefGoogle Scholar
  54. 54.
    Rittler, T., Nezveda, M., Seitner, F., Gelautz, M.: Depth-guided disocclusion inpainting for novel view synthesis. In: Proceedings of OAGM&ARW Joint Workshop Vision, Automation and Robotics, pp. 160–164 (2017).  https://doi.org/10.3217/978-3-85125-524-9-34
  55. 55.
    Wang, J., Cohen, M.: Image and video matting: a survey. Found. Trends® Comput. Graph. Vis. 3(2), 97–175 (2008)Google Scholar
  56. 56.
    Brosch, N., Nezveda, M., Gelautz, M., Seitner, F.: Efficient quality enhancement of disparity maps based on alpha matting. In: Proceedings of SPIE 9011, Stereoscopic Displays and Applications XXV, 90110M (2014).  https://doi.org/10.1117/12.2035361
  57. 57.
    Dumic, E., Grgic, S., Bermejo, D.J., Cruz, L.A.S.: benchmark of state of the art objective measures for 3D stereoscopic video quality assessment on the nantes database. In: Proceedings of the 56th International Symposium ELMAR-2014, pp. 119–123 (2014)Google Scholar
  58. 58.
    Urvoy, M., Barkowsky, M., Cousseau, R., Koudota, Y., Ricorde, V., Le Callet, P., Gutierrez, J., Garcia, N.: NAMA3DS1-COSPAD1: subjective video quality assessment database on coding conditions introducing freely available high quality 3D stereoscopic sequences. In: Quality of Multimedia Experience (QoMEX), pp. 109–114 (2012)Google Scholar
  59. 59.
    Bosc, E., Le Callet, P., Morin, L., Pressigout, M.: Visual quality assessment of synthesized views in the context of 3DTV. In: Zhu, C., Zhao, Y., Yu, L., Tanimoto, M. (eds.) 3D-TV system with Depth-Image-Based Rendering, pp. 439–473. Springer, New York (2013)CrossRefGoogle Scholar
  60. 60.
    Hewage, C.T.E.R., Worrall, S.T., Dogan, S., Kondoz, A.M.: Prediction of stereoscopic video quality using objective quality models of 2-D video. Electron. Lett. 44(16), 963–965 (2008)CrossRefGoogle Scholar
  61. 61.
    Benoit, A., Le Callet, P., Campisi, P., Cousseau, R.: Quality assessment of stereoscopic images. EURASIP J. Image Video Process. 2008, Article ID 659024, 13 p (2008)Google Scholar
  62. 62.
    Aflaki, P., Hannuksela, M.M., Hakala, J., Häkkinenb, J., Gabbouj, M.: Estimation of subjective quality for mixed-resolution stereoscopic video. In: 3DTV Conference: The True Vision—Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4 (2011)Google Scholar
  63. 63.
    Bosc, E., Pepion, R., Le Callet, P., Pressigout, M., Morin, L.: Reliability of 2D quality assessment methods for synthesized views evaluation in stereoscopic viewing conditions. In: 3DTV-Conference: The True Vision—Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4 (2012)Google Scholar
  64. 64.
    Wang, K., Barkowsky, M., Brunnström, K., Sjöström, M., Cousseau, R., Le Callet, P.: Perceived 3D TV transmission quality assessment: multi-laboratory results using absolute category rating on quality of experience scale. IEEE Trans. Broadcasting 58(4) (2012)Google Scholar
  65. 65.
    Conze, P.H., Robert, P., Morin, L.: Objective view synthesis quality assessment. In: Proceedings of SPIE, Stereoscopic Displays and Applications, Feb 2012Google Scholar
  66. 66.
    Battisti, F., Bosc, E., Carli, M., Le Callet, P., Perugia, S.: Objective image quality assessment of 3D synthesized views. Signal Process. Image Commun. 30, 78–88 (2015)CrossRefGoogle Scholar
  67. 67.
    Sandic-Stankovic, D., Kukolj, D., Le Callet, P.: DIBR synthesized image quality assessment based on morphological wavelets. International Workshop on Quality of Multimedia Experience QoMEX, Costa Navarino, Greece, May 2015Google Scholar
  68. 68.
    Sandic-Stankovic, D., Kukolj, D., Le Callet, P.: DIBR synthesized image quality assessment based on morphological pyramids. In: 3DTV-CON Immersive and Interactive 3D Media Experience Over Networks, Lisbon, July 2015Google Scholar
  69. 69.
    Ekmekcioglu, E., Worall, S.T., De Silva, D., Fernando, W.A.C., Kondoz, A.M.: Depth based perceptual quality assessment for synthesized camera viewpoints. In: International Conference on User Centric Media, Sept 2010Google Scholar
  70. 70.
    Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcasting 50(3), 312–322 (2004)CrossRefGoogle Scholar
  71. 71.
    Oh, K.J., Yea, S., Vetro, A., Ho, Y.S.: Virtual view synthesis method and self evaluation metrics for free viewpoint television and 3D video. Int. J. Imaging Syst. Technol. (2010)Google Scholar
  72. 72.
    Shahid Farid, M., Lucenteforte, M., Grangetto, M.: Objective quality metric for 3D virtual views. In: International Conference on Image processing, ICIP (2015)Google Scholar
  73. 73.
    Solh, M., AlRegib, G.: MIQM: a novel multi-view images quality measure. The First International Workshop on Quality of Multimedia Experience (QOMEX), San Diego, CA, 29–31 July (2009)Google Scholar
  74. 74.
    Shao, H., Cao, X., Er, G.: Objective quality assessment of depth image based rendering in 3DTV system. 3DTV-CON (2009)Google Scholar
  75. 75.
    Hanhart, P., Bosc, E., Le Callet, P., Ebrahimi, T.: Free-viewpoint video sequences: a new challenge for objective quality metrics. International Workshop on Multimedia Signal Processing (MMSP), Jakarta, Indonesia (2014)Google Scholar
  76. 76.
    Solh, M., AlRegib, G., Bauza, J.M.: 3VQM: a vision-based quality measure for DIBR-based 3D videos. In: IEEE International Conference on Multimedia and Expo (ICME), 1–6, July 2011Google Scholar
  77. 77.
    Song, R., Ko, H., Kuo, C.C.J.: MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source. J. Vis. Commun. Image Represent. (2014)Google Scholar
  78. 78.
    Bosc, E., Battisti, F., Carli, M., Callet, P.L.: A wavelet-based image quality metric for the assessment of 3D synthesized views. In: Proceedings of SPIE 8648, Stereoscopic Displays and Applications, Mar 2013Google Scholar
  79. 79.
    El-Yamany, N., Ugur, K., Hannuksela, Gabbouj, M.: Evaluation of depth compression and view synthesis distortions in multiview-video-plus-depth coding systems. In: 2DTV-CON (2010)Google Scholar
  80. 80.
    Dumic, E., Grgic, S., Cruz, L.A.S., Assuncao, P.: Objective quality measures comparison of impaired 3D video sequences from the UC3D database. In: Proceedings of 3DTV-Conference 2014: In Pursuit of Next Generation 3D Display (2014)Google Scholar
  81. 81.
    Tikanmaki, A., Gotchev, A., Smolic, A., Miller, K.: Quality assessment of 3D video in rate allocation experiments. In: IEEE Symposium on Consumer Electronics, pp. 1–4, Apr 2008Google Scholar
  82. 82.
    Silva, D., Fernando, W., Worrall, S., Kondoz, A.: A novel depth map quality metric and its usage in depth map coding. In: 3DTV/Conference (2011)Google Scholar
  83. 83.
    Dehkordi, A.B., Pourzad, M., Nasiopoulos, P.: A study on the relationship between depth map quality and the overall 3D video quality of experience. In: 3DTV-Conference (2013)Google Scholar
  84. 84.
    Do, L., Zinger, S., de With, P.: Quality improving techniques for free-viewpoint DIBR. In: Proceedings of SPIE Stereoscopic displays and applications (2010)Google Scholar
  85. 85.
    Kooi, F.L., Toet, A.: Visual comfort of binocular and 3D displays. Displays 25(2), 99–108 (2004)CrossRefGoogle Scholar
  86. 86.
    Schneider, B., Moraglia, G., Jepson, A.: Binocular unmasking: An analog to binaural unmasking? Science 243(4897), 1479–1482 (1989)CrossRefGoogle Scholar
  87. 87.
    Meesters, L.M., IJsselsteijn, W.A., Seuntiëns, P.J.: A survey of perceptual evaluations and requirements of three-dimensional TV. IEEE Trans. Circuits Syst. Video Technol. 14(3), 381–391 (2004)Google Scholar
  88. 88.
    Lambooij, M.T., IJsselsteijn, W.A., Heynderickx, I.: Visual discomfort in stereoscopic displays: a review. In: Proceedings of SPIE, vol. 6490 (2007)Google Scholar
  89. 89.
    Ostrin, L.A., Glasser, A.: Accommodation measurements in a prepresbyopic and presbyopic population. J. Cataract Refract. Surg. 30(7), 1435–1444 (2004)CrossRefGoogle Scholar
  90. 90.
    Rushton, S.K., Riddell, P.M.: Developing visual systems and exposure to virtual reality and stereo displays: some concerns and speculations about the demands on accommodation and vergence. Appl. Ergon. 30(1), 69–78 (1999)CrossRefGoogle Scholar
  91. 91.
    Babrow, A.S.: Theory and method in research on audience motives. J. Broadcast. Electron. Media 32(4), 471–487 (1988)CrossRefGoogle Scholar
  92. 92.
    Nisbett, R.E., Wilson, T.D.: Telling more than we can know: verbal reports on mental processes. Psychol. Rev. 84(3), 231 (1977)CrossRefGoogle Scholar
  93. 93.
    Ravaja, N.: Contributions of psychophysiology to media research: review and recommendations. Media Psychol. 6(2), 193–235 (2004)CrossRefGoogle Scholar
  94. 94.
    Pereda-Baños, A., Barreda-Ángeles, M.: On human information processing in information retrieval. In: NeuroIR’2015 13 Aug 2015, Santiago, Chile (2015)Google Scholar
  95. 95.
    Bonomi, M., Barreda-Ángeles, M., Battisti, F., Boato, G., Le Callet, P., Carli, M.: Towards QoE estimation of 3D contents through non-invasive methods. In: 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4. IEEE, July 2016Google Scholar
  96. 96.
    Kahneman, D.: Attention and Effort. Prentice-Hall, Englewood Cliffs (1973)Google Scholar
  97. 97.
    Wickens, C.D.: Processing resources in attention, dual task performance, and workload assessment. Technical Report EPL-81–3/ONR-81-3, University of Illinois at Urbana (1981)Google Scholar
  98. 98.
    Milán, E.G., González, A., Sanabria, D., Pereda, A., Hochel, M.: The nature of residual cost in regular switch response factors. Acta Physiol. 122(1), 45–57 (2006)Google Scholar
  99. 99.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): technical manual and affective ratings. In: NIMH Center for the Study of Emotion and Attention, pp. 39–58 (1997)Google Scholar
  100. 100.
    LeDoux, J.: Rethinking the emotional brain. Neuron 73(4), 653–676 (2012)CrossRefGoogle Scholar
  101. 101.
    Armony & LeDoux. In: Rees, A., Palmer, A.R. (eds.) The Oxford Handbook of Auditory Science: The Auditory Brain, vol. 2, pp. 479–505. Oxford University Press, NY (2010)Google Scholar
  102. 102.
    Larsson, P., Västfjäll, D.: Emotional and behavioural responses to auditory interfaces in commercial vehicles. Int. J. Veh. Noise Vib. 9(1–2), 75–95 (2013)CrossRefGoogle Scholar
  103. 103.
    Vorderer, P., Steen, F.F., Chan, E.: Motivation. In: Bryant, J., Vorderer, P. (eds.) Psychology of entertainment, pp. 3–17. Laurence Erlbaum, Mahwah, NJ (2006)Google Scholar
  104. 104.
    Döveling, K., von Scheve, C., Konijn, E.A. (eds.): The Routledge handbook of emotions and mass media. Routledge, London (2011)Google Scholar
  105. 105.
    Barreda-Ángeles, M., Pépion, R., Bosc, E., Le Callet, P., Pereda-Baños, A.: Exploring the effects of 3D visual discomfort on viewers’ emotions. In: IEEE International Conference on Image Processing (ICIP), pp. 753–757. IEEE, Oct 2014Google Scholar
  106. 106.
    Bradley, M.M., Lang, P.J.: The international affective digitized sounds (IADS-2): affective ratings of sounds and instruction manual. University of Florida, Gainesville, FL, Technical Report. B-3 (2007)Google Scholar
  107. 107.
    Reeves, B., Nass, C.: How People Treat Computers, Television, and New Media Like Real People and Places. CSLI Publications and Cambridge University Press, pp. 3–18 (1996)Google Scholar
  108. 108.
    Zillmann, D.: Exemplification effects in the promotion of safety and health. J. Commun. 56(s1) (2006)Google Scholar
  109. 109.
    Zillmann, D.: Mechanisms of emotional reactivity to media entertainments. In: The Routledge Handbook of Emotions and Mass Media, pp. 101–115 (2011)Google Scholar
  110. 110.
    Vorderer, P.A., Hartmann, T.: Entertainment and enjoyment as media effects (2009)Google Scholar
  111. 111.
    Vorderer, P.: Audience involvement and program loyalty. Poetics 22(1–2), 89–98 (1993)CrossRefGoogle Scholar
  112. 112.
    Urvoy, M., Barkowsky, M., Le Callet, P.: How visual fatigue and discomfort impact 3D-TV quality of experience: a comprehensive review of technological, psychophysical, and psychological factors. Ann. Telecommu. (annales des télécommunications) 68(11–12), 641–655Google Scholar
  113. 113.
    Clifford, C.W., Webster, M.A., Stanley, G.B., Stocker, A.A., Kohn, A., Sharpee, T.O., Schwartz, O.: Visual adaptation: neural, psychological and computational aspects. Vis. Res. 47(25), 3125–3131 (2007)CrossRefGoogle Scholar
  114. 114.
    Sanchez-Vives, M.V., Slater, M.: From presence to consciousness through virtual reality. Nat. Rev. Neurosci. 6(4), 332–339 (2005)CrossRefGoogle Scholar
  115. 115.
    Minsky, M.: Telepresence (1980)Google Scholar
  116. 116.
    Peters, C., Pelachaud, C., Bevacqua, E., Mancini, M., Poggi, I.: A model of attention and interest using gaze behavior. In: Lecture Notes in Computer Science, vol. 3661, pp. 229–240 (2005)Google Scholar
  117. 117.
    Fredricks, J.A., Blumenfeld, P.C., Paris, A.H.: School engagement: potential of the concept, state of the evidence. Rev. Educ. Res. 74(1), 59–109 (2004)CrossRefGoogle Scholar
  118. 118.
    Mason, M.F., Norton, M.I., Van Horn, J.D., Wegner, D.M., Grafton, S.T., Macrae, C.N.: Wandering minds: the default mode network and stimulus-independent thought. Science 315(5810), 393–395 (2007)CrossRefGoogle Scholar
  119. 119.
    Arapakis, I., Barreda-Angeles, M., Pereda-Banos, A.: Interest as a proxy of engagement in news reading: spectral and entropy analyses of eeg activity patterns. IEEE Trans. Affect. Comput. (2017)Google Scholar
  120. 120.
    Hendrix, C., Barfield, W.: Presence within virtual environments as a function of visual display parameters. Presence Teleoperators Virtual Environ. 5(3), 274–289 (1996)CrossRefGoogle Scholar
  121. 121.
    Shilling, R.D., Shinn-Cunningham, B.: Virtual auditory displays. In: Handbook of Virtual Environment Technology, pp. 65–92 (2002)Google Scholar
  122. 122.
    Murray, C.D., Arnold, P., Thornton, B.: Presence accompanying induced hearing loss: Implications for immersive virtual environments. Presence Teleoperators Virtual Environ. 9, 137–148 (2000)CrossRefGoogle Scholar
  123. 123.
    Cengarle, G., Pereda-Baños, A.: The perception of masked sounds and reverberation in 3D versus 2D playback systems. In: Proceedings of 134th AES Convention, May 2013Google Scholar
  124. 124.
    Gorzel, M., Corrigan, D., Kearney, G., Squires, J., Boland, F.: Distance perception in virtual audio-visual environments. In: Proceedings of 25th AES UK Conference: Spatial Audio in Today’s 3D World. AES, UK (2012)Google Scholar
  125. 125.
    Silzle, A., George, S., Habets, E.A.P., Bachmann, T.: Investigation on the quality of 3D sound reproduction. In: Proceedings of ICSA, p. 334 (2011)Google Scholar
  126. 126.
    Riecke, B.E., Väljamäe, A., Schulte-Pelkum, J.: Moving sounds enhance the visually-induced self-motion illusion (circular vection) in virtual reality. ACM Trans. Appl. Percept. (TAP) 6(2), 7 (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Miguel Barreda-Ángeles
    • 1
    Email author
  • Federica Battisti
    • 2
  • Giulia Boato
    • 3
  • Marco Carli
    • 2
  • Emil Dumic
    • 4
  • Margrit Gelautz
    • 5
  • Chaminda Hewage
    • 6
  • Dragan Kukolj
    • 7
  • Patrick Le-Callet
    • 8
  • Antonio Liotta
    • 9
  • Cecilia Pasquini
    • 10
  • Alexandre Pereda-Baños
    • 1
  • Christos Politis
    • 11
  • Dragana Sandic
    • 7
  • Murat Tekalp
    • 12
  • María Torres-Vega
    • 9
  • Vladimir Zlokolica
    • 7
  1. 1.EurecatBarcelonaSpain
  2. 2.University of Roma TRERomeItaly
  3. 3.University of TrentoTrentoItaly
  4. 4.Department of Electrical EngineeringUniversity NorthVaraždinCroatia
  5. 5.Vienna University of TechnologyViennaAustria
  6. 6.Cardiff Metropolitan UniversityCardiffUK
  7. 7.Unviersity of Novi SadNovi SadSerbia
  8. 8.University of NantesNantesFrance
  9. 9.Eindhoven University of TechnologyEindhovenThe Netherlands
  10. 10.University of InnsbruckInnsbruckAustria
  11. 11.Kingston University LondonLondonUK
  12. 12.Koç UniversityIstanbulTurkey

Personalised recommendations

Cite chapter

Buy options