Automated and robust portrait quality assessment (PQA) is of paramount importance in high-impact applications such as smartphone photography. This paper presents FHIQA, a learning-based approach to PQA that introduces a simple but effective quality score rescaling method based on image semantics, to enhance the precision of fine-grained image quality metrics while ensuring robust generalization to various scene settings beyond the training dataset. The proposed approach is validated by extensive experiments on the PIQ23 benchmark and comparisons with the current state of the art. The source code of FHIQA will be made publicly available on the PIQ23 GitHub repository at https://github.com/DXOMARK-Research/PIQ2023.

翻译：自动化且鲁棒的肖像质量评估在智能手机摄影等高影响力应用中至关重要。本文提出了一种基于学习的肖像质量评估方法FHIQA，该方法引入了一种简单但有效的基于图像语义的质量分数重标定方法，旨在提升细粒度图像质量指标的精确性，同时确保对训练数据集以外的各种场景设定具有鲁棒泛化能力。通过在PIQ23基准上的大量实验以及与当前最先进方法的比较，验证了所提方法的有效性。FHIQA的源代码将在PIQ23 GitHub仓库（https://github.com/DXOMARK-Research/PIQ2023）上公开发布。