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

翻译：年复一年，智能手机照片质量持续提升的需求不断增长，尤其在肖像摄影领域。因此，制造商在智能手机摄像头开发过程中始终贯穿使用感知质量标准。这一昂贵流程可部分由基于学习的自动化图像质量评估（IQA）方法替代。由于IQA具有主观性，需评估并保证其流程的一致性，而现有广泛用于众包IQA的平均意见分数（MOS）缺乏这一特性。此外，现有盲图像质量评估（BIQA）数据集较少关注跨内容评估的难度，这可能导致标注质量下降。本文提出PIQ23——一个面向人像的专用IQA数据集，包含由100款智能手机在50个预设场景中采集的5116张图像，覆盖多种品牌、型号及使用场景。数据集包含来自不同性别和种族的个人，他们已明确知情同意其照片用于公开研究。该数据集通过从30余位图像质量专家收集的成对比较（PWC）进行标注，涵盖三个图像属性：面部细节保留、面部目标曝光及整体图像质量。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，通过与现有基线的广泛比较，我们证明语义信息（图像上下文）可用于改进IQA预测。该数据集连同所提出的统计分析及BIQA算法均可获取：https://github.com/DXOMARK-Research/PIQ2023