聚焦短视频用户生成内容的画质评估与增强，推动轻量化模型发展。
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
- 设计轻量级视频画质评估模型，摒弃复杂集成与冗余参数。
- 构建1800对合成+1900张真实短视频图像数据集，支持超分辨率研究。
- 适合关注短视频平台画质优化的研究者与工程师参考。
本文回顾了NTIRE 2025短形式用户生成视频质量评估与增强挑战赛。比赛包含两个赛道：(i) 高效视频质量评估（KVQ），旨在推进轻量化、高效的视频质量评估模型发展，减少对模型集成、冗余权重等计算开销大的组件依赖；(ii) 基于扩散的图像超分辨率（KwaiSR）。该赛道引入专为单图超分辨率设计的短形式用户生成内容数据集——KwaiSR，包含1,800对合成的S-UGC图像对和1,900张真实S-UGC图像，按8:1:1比例划分为训练、验证与测试集。挑战赛目标是提升如Kwai、TikTok等短形式UGC平台的用户体验。共有266名参与者，提交18份有效最终方案及配套说明文档，显著推动了短形式UGC视频质量评估与图像超分辨率的发展。项目开源地址：https://github.com/lixinustc/KVQE-ChallengeCVPR-NTIRE2025。
原文摘要 · Abstract (English)
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusion-based Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE- ChallengeCVPR-NTIRE2025.
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