TY - GEN
T1 - No-Reference Stereoscopic Video Quality Assessment Based on Spatial-Temporal Statistics
AU - Zhang, Jiufa
AU - Liu, Lixiong
AU - Gong, Jiachao
AU - Huang, Hua
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Stereoscopic video quality assessment (SVQA) has become the necessary support for 3D video processing while the research on efficient SVQA method faces enormous challenge. In this paper, we propose a novel blind SVQA method based on monocular and binocular spatial-temporal statistics. We first extract the frames and the frame difference maps from adjacent frames of both left and right view videos as the spatial and spatial-temporal representation of the video content, and then use the local binary pattern (LBP) operator to calculate spatial and temporal domains’ statistical features. Besides, we simulate binocular fusion perception by performing weighted integration of generated monocular statistics to obtain binocular scene statistics and motion statistics. Finally, all the computed features are utilized to train the stereoscopic video quality prediction model by a support vector regression (SVR). The experimental results show that our proposed method achieves better performance than state-of-the-art SVQA approaches on three public databases.
AB - Stereoscopic video quality assessment (SVQA) has become the necessary support for 3D video processing while the research on efficient SVQA method faces enormous challenge. In this paper, we propose a novel blind SVQA method based on monocular and binocular spatial-temporal statistics. We first extract the frames and the frame difference maps from adjacent frames of both left and right view videos as the spatial and spatial-temporal representation of the video content, and then use the local binary pattern (LBP) operator to calculate spatial and temporal domains’ statistical features. Besides, we simulate binocular fusion perception by performing weighted integration of generated monocular statistics to obtain binocular scene statistics and motion statistics. Finally, all the computed features are utilized to train the stereoscopic video quality prediction model by a support vector regression (SVR). The experimental results show that our proposed method achieves better performance than state-of-the-art SVQA approaches on three public databases.
KW - No-reference
KW - Spatial-temporal
KW - Stereoscopic video quality assessment
KW - Structural statistics
UR - http://www.scopus.com/inward/record.url?scp=85076836137&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34113-8_8
DO - 10.1007/978-3-030-34113-8_8
M3 - Conference contribution
AN - SCOPUS:85076836137
SN - 9783030341121
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 94
BT - Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 3
A2 - Zhao, Yao
A2 - Lin, Chunyu
A2 - Barnes, Nick
A2 - Chen, Baoquan
A2 - Westermann, Rüdiger
A2 - Kong, Xiangwei
PB - Springer
T2 - 10th International Conference on Image and Graphics, ICIG 2019
Y2 - 23 August 2019 through 25 August 2019
ER -