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Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos
| Content Provider | MDPI |
|---|---|
| Author | Jiang, Jiu Wang, Xianpei Li, Bowen Tian, Meng Yao, Hongtai |
| Copyright Year | 2021 |
| Description | Over the past few decades, video quality assessment (VQA) has become a valuable research field. The perception of in-the-wild video quality without reference is mainly challenged by hybrid distortions with dynamic variations and the movement of the content. In order to address this barrier, we propose a no-reference video quality assessment (NR-VQA) method that adds the enhanced awareness of dynamic information to the perception of static objects. Specifically, we use convolutional networks with different dimensions to extract low-level static-dynamic fusion features for video clips and subsequently implement alignment, followed by a temporal memory module consisting of recurrent neural networks branches and fully connected (FC) branches to construct feature associations in a time series. Meanwhile, in order to simulate human visual habits, we built a parametric adaptive network structure to obtain the final score. We further validated the proposed method on four datasets (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) to test the generalization ability. Extensive experiments have demonstrated that the proposed method not only outperforms other NR-VQA methods in terms of overall performance of mixed datasets but also achieves competitive performance in individual datasets compared to the existing state-of-the-art methods. |
| Starting Page | 5322 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21165322 |
| Journal | Sensors |
| Issue Number | 16 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-06 |
| Access Restriction | Open |
| Subject Keyword | Sensors Information and Library Science Video Quality Assessment Multidimensional Features Convolutional Neural Network Recurrent Neural Networks |
| Content Type | Text |
| Resource Type | Article |
