NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

TL;DR

The NTIRE 2024 challenge focuses on evaluating and advancing image and video quality assessment methods specifically for AI-generated content, involving extensive participation and new benchmark datasets.

Contribution

This paper introduces the NTIRE 2024 challenge, new datasets, and evaluation benchmarks for AI-generated image and video quality assessment, fostering progress in this emerging field.

Findings

Winning methods outperform baselines in prediction accuracy.

Large-scale datasets enable robust evaluation of AIGC quality assessment models.

Significant participation indicates strong community interest and progress.

Abstract

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.