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.

翻译：本文报告了NTIRE 2024 AI生成内容质量评估挑战赛的相关情况，该挑战赛将与CVPR 2024上的“图像复原与增强新趋势研讨会”（NTIRE）联合举办。本次挑战旨在解决图像与视频处理领域的一大难题，即针对AI生成内容（AIGC）的图像质量评估（IQA）与视频质量评估（VQA）。挑战赛分为图像赛道和视频赛道。图像赛道采用AIGIQA-20K数据集，该数据集包含由15种主流生成模型生成的20,000张AI生成图像（AIGI）。图像赛道共有318名注册参与者，开发阶段共收到1,646份提交，测试阶段收到221份提交，最终有16支参赛团队提交了模型与事实表。视频赛道采用T2VQA-DB数据集，包含由9种主流文本生成视频（T2V）模型生成的10,000个AI生成视频（AIGV）。共有196名参与者注册了视频赛道，开发阶段收到991份提交，测试阶段收到185份提交，最终有12支参赛团队提交了模型与事实表。部分方法取得了优于基线方法的结果，两个赛道的优胜方法均展现出对AIGC更优的预测性能。