Summary of Finevq: Fine-grained User Generated Content Video Quality Assessment, by Huiyu Duan et al.
FineVQ: Fine-Grained User Generated Content Video Quality Assessment
by Huiyu Duan, Qiang Hu, Jiarui Wang, Liu Yang, Zitong Xu, Lu Liu, Xiongkuo Min, Chunlei Cai, Tianxiao Ye, Xiaoyun Zhang, Guangtao Zhai
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to video quality assessment, aiming to address the limitations of current methods that provide only overall ratings for user-generated content (UGC) videos. The authors introduce the Fine-grained Video quality assessment Database (FineVD), comprising 6104 UGC videos with fine-grained quality scores and descriptions across multiple dimensions. They also propose a Fine-grained Video Quality assessment (FineVQ) model to learn the fine-grained quality of UGC videos, enabling quality rating, scoring, and attribution. The proposed approach outperforms state-of-the-art methods on various benchmark datasets, including FineVD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it easier to judge how good or bad a video is by creating a special database with lots of different ways to measure video quality. This helps make videos better for people who like them and makes recommendations more accurate. The new model they created can rate videos in many different ways, not just saying if they’re good or bad overall. |
