  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
