We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA datasets, ours focuses on highly aesthetic photos of high technical quality, filling a gap in the literature. The images, carefully curated to exclude synthetic content, are sufficiently diverse to train general NR-IQA models. The dataset is annotated with perceptual quality ratings obtained through a crowdsourcing study. Ten expert raters, comprising photographers and graphics artists, assessed each image at least twice in multiple sessions spanning several days, resulting in highly reliable labels. Annotators were rigorously selected based on several metrics, including self-consistency, to ensure their reliability. The dataset includes rich metadata with user and machine-generated tags from over 5,000 categories and popularity indicators such as favorites, likes, downloads, and views. With its unique characteristics, such as its focus on high-quality images, reliable crowdsourced annotations, and high annotation resolution, our dataset opens up new opportunities for advancing perceptual image quality assessment research and developing practical NR-IQA models that apply to modern photos. Our dataset is available at https://database.mmsp-kn.de/uhd-iqa-benchmark-database.html

翻译：我们提出了一个新颖的图像质量评估数据集，包含6073张UHD-1（4K）图像，其标注固定宽度为3840像素。与现有的无参考图像质量评估数据集不同，我们的数据集专注于具有高度美学价值和技术质量的照片，填补了文献中的空白。这些图像经过精心筛选以排除合成内容，其多样性足以训练通用的无参考图像质量评估模型。数据集的标注通过众包研究获得感知质量评分。十位专家评分者（包括摄影师和图形艺术家）在跨越数日的多个会话中，对每张图像至少评估两次，从而产生了高度可靠的标签。评分者根据包括自一致性在内的多项指标严格筛选，以确保其可靠性。数据集包含丰富的元数据，涵盖来自超过5000个类别的用户和机器生成标签，以及收藏、点赞、下载和浏览等流行度指标。凭借其独特特性，如专注于高质量图像、可靠的众包标注和高标注分辨率，我们的数据集为推进感知图像质量评估研究以及开发适用于现代照片的实用无参考图像质量评估模型开辟了新的机遇。我们的数据集可在 https://database.mmsp-kn.de/uhd-iqa-benchmark-database.html 获取。