人像图像质量评估数据集
An Image Quality Assessment Dataset for Portraits
AI总结：
本文提出PIQ23人像IQA数据集，含100部手机拍摄的5116张图像，由专家成对比较标注三个属性，并证明语义信息可提升盲图像质量评估预测性能。
摘要：
年复一年，人们对更好的智能手机照片的需求持续增长，尤其是在人像摄影领域。因此，制造商在智能手机相机的整个开发过程中使用感知质量标准。这一昂贵的流程可以部分由基于学习的自动化图像质量评估（IQA）方法替代。由于其主观性，有必要估计并保证IQA过程的一致性，而广泛用于众包IQA的平均意见得分（MOS）缺乏这一特性。此外，现有的盲图像质量评估（BIQA）数据集很少关注跨内容评估的难度，这可能会降低标注质量。本文介绍了PIQ23，一个针对人像的IQA数据集，包含由100部智能手机采集的50个预定义场景的5116张图像，覆盖了广泛的品牌、型号和使用场景。该数据集包含不同性别和族裔的个人，他们已明确知情同意其照片用于公共研究。该数据集通过从30多位图像质量专家收集的成对比较（PWC）进行标注，针对三个图像属性：人脸细节保留、人脸目标曝光和整体图像质量。对这些标注的深入统计分析使我们能够评估其在PIQ23上的一致性。最后，我们通过与现有基线的广泛比较表明，语义信息（图像上下文）可用于改进IQA预测。该数据集以及所提出的统计分析和BIQA算法可在以下地址获取：https://github.com/DXOMARK-Research/PIQ2023
英文摘要：
Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA). Due to its subjective nature, it is necessary to estimate and guarantee the consistency of the IQA process, a characteristic lacking in the mean opinion scores (MOS) widely used for crowdsourcing IQA. In addition, existing blind IQA (BIQA) datasets pay little attention to the difficulty of cross-content assessment, which may degrade the quality of annotations. This paper introduces PIQ23, a portrait-specific IQA dataset of 5116 images of 50 predefined scenarios acquired by 100 smartphones, covering a high variety of brands, models, and use cases. The dataset includes individuals of various genders and ethnicities who have given explicit and informed consent for their photographs to be used in public research. It is annotated by pairwise comparisons (PWC) collected from over 30 image quality experts for three image attributes: face detail preservation, face target exposure, and overall image quality. An in-depth statistical analysis of these annotations allows us to evaluate their consistency over PIQ23. Finally, we show through an extensive comparison with existing baselines that semantic information (image context) can be used to improve IQA predictions. The dataset along with the proposed statistical analysis and BIQA algorithms are available: https://github.com/DXOMARK-Research/PIQ2023
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
