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In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm.
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A. Mittal, A. K. Moorthy, and A. C. Bovik, · 2012
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A. Mittal, R. Soundararajan, and A. C. Bovik, · 2012
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Image processing, analysis, and machine vision
M. Sonka, V. Hlavac, and R. Boyle, · 2014
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M. A. Saad, A. C. Bovik, and C. Charrier, · 2014
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J. Xue, D.-Q. Zhang, H. Yu, and C. W. Chen, · 2014
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X. Yin, A. Jindal, V. Sekar, and B. Sinopoli, · 2015
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A. Mittal, M. A. Saad, and A. C. Bovik, · 2015
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N. Venkatanath, D. Praneeth, M. C. Bh, S. S. Channappayya, and S. S. Medasani, · 2015
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“SDNDASH: Improving QoE of HTTP adaptive streaming using software defined networking,”
A. Bentaleb, A. C. Begen, and R. Zimmermann, · 2016
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“A quality-of-experience index for streaming video,”
Z. Duanmu, K. Zeng, K. Ma, A. Rehman, and Z. Wang, · 2016
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“Blind image quality assessment using statistical structural and luminance features,”
Q. Li, W. Lin, J. Xu, and Y. Fang, · 2016
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“Deep residual learning for image recognition,”
“A knowledge-driven quality-of-experience model for adaptive streaming videos,”
Z. Duanmu, W. Liu, D. Chen, Z. Li, Z. Wang, et al., · 2019
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Cisco, · 2020
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G. Zhai and X. Min, · 2020
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“Fast multi-rate encoding for adaptive http streaming,”
H. Amirpour, E. Çetinkaya, C. Timmerer, and M. Ghanbari, · 2020
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K. He, X. Zhang, S. Ren, and J. Sun, · 2016
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L. Krasula, K. Fliegel, P. Le Callet, and M. Klíma, · 2016
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“Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment,”
J. Kim, H. Zeng, D. Ghadiyaram, S. Lee, L. Zhang, and A. C. Bovik, · 2017
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“Learning to predict streaming video qoe: Distortions, rebuffering and memory,”
C. G. Bampis and A. C. Bovik, · 2017
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“Learning a continuous-time streaming video QoE model,”
D. Ghadiyaram, J. Pan, and A. C. Bovik, · 2018
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“A quality-of-experience database for adaptive video streaming,”
Z. Duanmu, A. Rehman, and Z. Wang, · 2018
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“Towards perceptually optimized end-to-end adaptive video streaming,”
C. G. Bampis, Z. Li, I. Katsavounidis, T.-Y. Huang, C. Ekanadham, and A. C. Bovik, · 2018
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L. Krasula, K. Fliegel, and P. Le Callet, · 2020
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“Perceptual quality assessment of omnidirectional images as moving camera videos,”
X. Sui, K. Ma, Y. Yao, and Y. Fang, · 2021
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“RAPIQUE: Rapid and accurate video quality prediction of user generated content,”
Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, · 2021
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“Video qoe inference with machine learning,”
Tisa-Selma, A. Bentaleb, and S. Harous, · 2021
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“Intense: In-depth studies on stall events and quality switches and their impact on the quality of experience in http adaptive streaming,”
B. Taraghi, M. Nguyen, H. Amirpour, and C. Timmerer, · 2021
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“FAST-VQA: Efficient end-to-end video quality assessment with fragment sampling,”
H. Wu, C. Chen, J. Hou, L. Liao, A. Wang, W. Sun, Q. Yan, and W. Lin, · 2022
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“Subjective and objective quality of experience of free viewpoint videos,”
J. Yan, J. Li, Y. Fang, Z. Che, X. Xia, and Y. Liu, · 2022
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