Abstract
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 this https URL ChallengeCVPR-NTIRE2025.
Abstract (translated)
本文介绍了针对2025年NTIRE挑战赛的短形式用户生成内容（UGC）视频质量评估和增强的回顾。该挑战赛包含两个赛道：(i) 高效视频质量评估(KVQ)，以及(ii) 基于扩散方法的图像超分辨率(KwaiSR)。 赛道1旨在推动轻量级且高效的视频质量评估(VQA)模型的发展，重点在于消除对模型集成、冗余权重及其他计算成本较高的组件的依赖，在之前的IQA/VQA竞赛中这些问题普遍存在。赛道2引入了一个专为单张图像超分辨率设计的新短形式UGC数据集，即KwaiSR数据集。该数据集包括1,800对合成生成的S-UGC图像和1,900张真实世界的S-UGC图像，并按照8:1:1的比例分配到训练、验证和测试集合中。 挑战赛的主要目标是推动研究工作，提升像Kwai和TikTok这样的短形式UGC平台上的用户体验。该挑战吸引了266名参与者并收到了18份有效的最终提交作品及其对应的事实表，为短形式UGC视频质量评估和图像超分辨率领域的发展做出了重大贡献。 该项目在以下网址公开发布：[ChallengeCVPR-NTIRE2025](https://challengecvpr-ntire2025.org/)
URL
https://arxiv.org/abs/2504.13131
