MAE-VQA: an efficient and accurate end-to-end video quality assessment method for user generated content videos
In the digital age, the proliferation of user-generated content (UGC) videos presents unique challenges in maintaining video quality across diverse platforms. In this project, we propose Masked Auto-Encoder model for no-reference video quality assessment (NR-VQA) problem. To our best knowledge, we a...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/178566 |