  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.
