TQP: An Efficient Video Quality Assessment Framework for Adaptive Bitrate Video Streaming

被引:2
作者
Aslam, Muhammad Azeem [1 ,2 ]
Wei, Xu [2 ]
Ahmed, Nisar [3 ]
Saleem, Gulshan [4 ]
Zhu, Shuangtong [2 ]
Xu, Yimei [1 ]
Hu, Hongfei [1 ]
机构
[1] Xian Eurasia Univ, Sch Informat Engn, Xian 710065, Shaanxi, Peoples R China
[2] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China
[3] Univ Engn & Technol Lahore, Dept Comp Engn, Lahore 54890, Punjab, Pakistan
[4] Lahore Garrison Univ, Dept Comp Sci, Lahore, Punjab, Pakistan
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Streaming media; Quality assessment; Video recording; Accuracy; Quality of experience; Computer architecture; Training; Video quality; image quality assessment; rate adaption; video streaming; quality of experience; QoE; PREDICTION;
D O I
10.1109/ACCESS.2024.3418375
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The increasing popularity of video streaming services and the widespread accessibility of high-speed internet underscore the importance of delivering cost-effective and seamless streaming experiences. Shared internet connections may lead to varying speeds, impacting Quality of Experience (QoE). Rate adaptation techniques aim to ensure smooth video transmission, but overly optimistic adaptations can compromise user experience. Objective video quality assessment is crucial for efficient rate adaptation to ensure smooth QoE. This research proposes a novel method incorporating temporal channel shifting into Convolutional Neural Networks (CNN) for video quality assessment while maintaining the computational simplicity of a 2D CNN model. The proposed approach relies on the EfficientNet architecture, initially pre-trained on quality-aware images, and fine-tune it using datasets of rate-adaptive videos. The model is trained and evaluated on two benchmark datasets, namely "Waterloo sQoE III" and "LIVE Netflix II," which consist of rate-adaptive videos annotated with subjective quality scores. Experimental results encompass the evaluation of Pearson, Spearman, and Kendall correlation coefficients, along with the computation time ratio for the proposed approach. The outcomes reveal competitive scores of 0.795, 0.652, 0.772, and 0.216 for the "Live Netflix II dataset" and 0.782, 0.713, 0.721, and 0.230 for the "Waterloo sQoE III dataset." Our proposed method, compared to 24 approaches for "Waterloo sQoE III" and 25 for "LIVE Netflix II," attains the highest correlation scores while maintaining near-real-time processing efficiency. These results affirm the efficacy of our approach in accurately predicting human judgment (QoE) with computational efficiency.
引用
收藏
页码:88264 / 88278
页数:15
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