Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

被引:0
作者
Coskun, Sahin [1 ]
Nur Yilmaz, Gokce [2 ]
Battisti, Federica [3 ]
Alhussein, Musaed [4 ]
Islam, Saiful [2 ]
机构
[1] Gazi Univ, Grad Sch Nat & Appl Sci, Dept Elect Elect Engn, TR-06560 Ankara, Turkiye
[2] TED Univ, Dept Comp Engn, TR-06420 Ankara, Turkiye
[3] Univ Padua, Dept Informat Engn, I-35131 Padua, Italy
[4] King Saud Univ, Coll Comp & Informat Sci, Dept Comp Engn, POB 51178, Riyadh 11543, Saudi Arabia
关键词
3D video; stereoscopic vision; human vision system; quality of experience; 3D-video QoE evaluation metric; numerical methods; STEREOSCOPIC VIDEO; SALIENCY;
D O I
10.3390/jimaging9120281
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.
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页数:28
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共 78 条
  • [1] Towards a quality metric for dense light fields
    Adhikarla, Vamsi Kiran
    Vinkler, Marek
    Sumin, Denis
    Mantiuk, Rafal K.
    Myszkowski, Karol
    Seidel, Hans-Peter
    Didyk, Piotr
    [J]. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 3720 - 3729
  • [2] Appina B, 2018, IEEE IMAGE PROC, P2800, DOI 10.1109/ICIP.2018.8451693
  • [3] Full-Reference 3-D Video Quality Assessment Using Scene Component Statistical Dependencies
    Appina, Balasubramanyam
    Channappayya, Sumohana S.
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2018, 25 (06) : 823 - 827
  • [4] Appina B, 2017, INT CONF ACOUST SPEE, P2012, DOI 10.1109/ICASSP.2017.7952509
  • [5] Banitalebi-Dehkordi A, 2012, INT CONF 3D IMAG
  • [6] Saliency inspired quality assessment of stereoscopic 3D video
    Banitalebi-Dehkordi, Amin
    Nasiopoulos, Panos
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (19) : 26055 - 26082
  • [7] An efficient human visual system based quality metric for 3D video
    Banitalebi-Dehkordi, Amin
    Pourazad, Mahsa T.
    Nasiopoulos, Panos
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2016, 75 (08) : 4187 - 4215
  • [8] Banitalebi-Dehkordi A, 2013, INT CONF ACOUST SPEE, P3731, DOI 10.1109/ICASSP.2013.6638355
  • [9] No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications
    Barman, Nabajeet
    Jammer, Emmanuel
    Ghorashi, Seyed Ali
    Martini, Maria G.
    [J]. IEEE ACCESS, 2019, 7 : 74511 - 74527
  • [10] BAYRAK H, 2018, SIG PROCESS COMMUN, P1, DOI DOI 10.1109/SIU.2018.8404323