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Frame importance and temporal memory effect-based fast video quality assessment for user-generated content

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Abstract

User-generated content (UGC) has become increasingly popular, promoted by the widespread use of social media and mobile devices. Therefore, instant and immersive UGC video quality assessment is urgently needed to provide appropriate recommendations for video reviewers prior to distribution. However, existing methods are neither efficient at assessing UGC videos due to the expensive frame-by-frame process nor suitable for deployment on devices with limited computational capabilities because they require sophisticated GPU-dependent computation. In this paper, we propose a fast UGC video quality assessment method, named FastVQA, by considering both keyframe importance and human temporal memory effects. First, a novel key frame selection strategy based on feature entropy is developed to achieve efficient and accurate feature extraction. Inspired by human short-term and long-term memory effects, we design a temporal feature aggregation module by taking both local content details and global semantic information into consideration. Experimental results show that FastVQA can outperform the state-of-the-art (SOTA) methods on many datasets with significantly reduced CPU time, which implies that FastVQA can achieve a better balance between complexity and accuracy.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research work was supported in part by the National Science Foundation of China (U1903213) and the Natural Science Foundation of Sichuan Province (2022NSFSC0966).

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Authors and Affiliations

  1. China Telecom, Shanghai, China

    Yuan Zhang, Mingchuan Yang & Zijun Wu

  2. Xi’an Jiaotong University, Xi’an, Shaanxi, China

    Zhiwei Huang & Lijun He

  3. Sichuan Digital Economy Industry Development Research Institute, Chengdu, Sichuan, China

    Lijun He

Authors
  1. Yuan Zhang
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  2. Mingchuan Yang
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  3. Zhiwei Huang
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  4. Lijun He
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  5. Zijun Wu
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Contributions

Yuan Zhang contributed to the conception of the study; Mingchuan Yang and Zhiwei Huang performed the experiment; Lijun He and Zijun Wu contributed significantly to analysis and manuscript preparation; Yuan Zhang, Mingchuan Yang and Zhiwei Huang performed the data analyses and wrote the manuscript.

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Correspondence to Yuan Zhang.

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Zhang, Y., Yang, M., Huang, Z. et al. Frame importance and temporal memory effect-based fast video quality assessment for user-generated content. Appl Intell 53, 21517–21531 (2023). https://doi.org/10.1007/s10489-023-04624-2

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