Advancing Video Quality Assessment for AIGC
Xinli Yue, Jianhui Sun, Han Kong, Liangchao Yao, Tianyi Wang, Lei Li,, Fengyun Rao, Jing Lv, Fan Xia, Yuetang Deng, Qian Wang, Lingchen Zhao

TL;DR
This paper introduces a new video quality assessment method tailored for AI-generated videos, combining a novel loss function and S2CNet to better evaluate and improve frame consistency and content retention.
Contribution
The paper presents a novel loss function and S2CNet technique specifically designed for assessing and enhancing AI-generated video quality, addressing gaps in existing evaluation methods.
Findings
Outperforms existing VQA methods on AIGC Video dataset
Achieves 3.1% higher PLCC than previous state-of-the-art
Effectively reduces inter-frame quality discrepancies
Abstract
In recent years, AI generative models have made remarkable progress across various domains, including text generation, image generation, and video generation. However, assessing the quality of text-to-video generation is still in its infancy, and existing evaluation frameworks fall short when compared to those for natural videos. Current video quality assessment (VQA) methods primarily focus on evaluating the overall quality of natural videos and fail to adequately account for the substantial quality discrepancies between frames in generated videos. To address this issue, we propose a novel loss function that combines mean absolute error with cross-entropy loss to mitigate inter-frame quality inconsistencies. Additionally, we introduce the innovative S2CNet technique to retain critical content, while leveraging adversarial training to enhance the model's generalization capabilities.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · Medical Imaging and Analysis
MethodsFocus
