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Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training

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Abstract

Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents. Moreover, the video contents and distortions among existing datasets are quite different, which leads to poor performance of data-driven methods in the cross-dataset evaluation setting. To improve the performance of quality assessment models, we borrow intuitions from human perception, specifically, content dependency and temporal-memory effects of human visual system. To face the cross-dataset evaluation challenge, we explore a mixed datasets training strategy for training a single VQA model with multiple datasets. The proposed unified framework explicitly includes three stages: relative quality assessor, nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly predict relative quality, perceptual quality, and subjective quality. Experiments are conducted on four publicly available datasets for VQA in the wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental results verify the effectiveness of the mixed datasets training strategy and prove the superior performance of the unified model in comparison with the state-of-the-art models. For reproducible research, we make the PyTorch implementation of our method available at https://github.com/lidq92/MDTVSFA.

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Notes

  1. Video-level features of BRISQUE are the average pooling of its frame-level features.

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Acknowledgements

This work was partially supported by the Natural Science Foundation of China under contracts 61572042, 61520106004, and 61527804. This work was also supported in part by National Key R&D Program of China (2018YFB1403900). We acknowledge the High-Performance Computing Platform of Peking University for providing computational resources.

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Authors and Affiliations

  1. National Engineering Laboratory for Video Technology, Peking University, Beijing, China

    Dingquan Li & Tingting Jiang

  2. Advanced Institute of Information Technology, Peking University, Hangzhou, China

    Dingquan Li

  3. Department of Computer Science, Peking University, Beijing, China

    Tingting Jiang

  4. Laboratory of Mathematics and Its Applications, School of Mathematical Sciences, Peking University, Beijing, China

    Dingquan Li & Ming Jiang

  5. Beijing International Center for Mathematical Research, Peking University, Beijing, China

    Dingquan Li

  6. Advanced Innovation Center for Future Visual Entertainment, Beijing Film Academy, Beijing, China

    Tingting Jiang

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  1. Dingquan Li
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Li, D., Jiang, T. & Jiang, M. Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training. Int J Comput Vis 129, 1238–1257 (2021). https://doi.org/10.1007/s11263-020-01408-w

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