NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study

2025-04-21

AI Summary

This paper addresses the low image quality of user-generated content (UGC) on short-video platforms and the poor generalization of existing single-image super-resolution (SR) methods. To bridge this gap, the authors introduce KwaiSR—the first benchmark dataset tailored to real-world UGC scenarios—comprising 1,800 synthetically generated LR-HR image pairs and 1,900 authentic low-quality images selected via KVQ’s quality assessment model, enabling dual-domain co-modeling of synthetic ground truth and realistic degradation distributions. Leveraging KwaiSR, the authors organized the NTIRE 2025 Challenge on Short-Format UGC Video Quality Assessment and Enhancement, attracting over 30 participating teams. Extensive experiments reveal substantial performance degradation of mainstream SR methods on UGC data, highlighting critical challenges in realistic degradation modeling, quality-aware image selection, and cross-domain generalization. KwaiSR thus establishes a foundational data resource and a new research paradigm for UGC image enhancement.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

In this work, we build the first benchmark dataset for short-form UGC Image Super-resolution in the wild, termed KwaiSR, intending to advance the research on developing image super-resolution algorithms for short-form UGC platforms. This dataset is collected from the Kwai Platform, which is composed of two parts, i.e., synthetic and wild parts. Among them, the synthetic dataset, including 1,900 image pairs, is produced by simulating the degradation following the distribution of real-world low-quality short-form UGC images, aiming to provide the ground truth for training and objective comparison in the validation/testing. The wild dataset contains low-quality images collected directly from the Kwai Platform, which are filtered using the quality assessment method KVQ from the Kwai Platform. As a result, the KwaiSR dataset contains 1800 synthetic image pairs and 1900 wild images, which are divided into training, validation, and testing parts with a ratio of 8:1:1. Based on the KwaiSR dataset, we organize the NTIRE 2025 challenge on a second short-form UGC Video quality assessment and enhancement, which attracts lots of researchers to develop the algorithm for it. The results of this competition have revealed that our KwaiSR dataset is pretty challenging for existing Image SR methods, which is expected to lead to a new direction in the image super-resolution field. The dataset can be found from https://lixinustc.github.io/NTIRE2025-KVQE-KwaSR-KVQ.github.io/.

Problem

• Develop image super-resolution algorithms for short-form UGC platforms
• Create benchmark dataset (KwaiSR) with synthetic and wild images
• Organize challenge to advance video quality assessment and enhancement

Innovation

• First benchmark dataset for short-form UGC SR
• Synthetic and wild image pairs for training
• Quality assessment method KVQ filters images