PICNIQ: Pairwise Comparisons for Natural Image Quality Assessment
Nicolas Chahine Sira Ferradans Jean Ponce
Blind image quality assessment (BIQA) approaches, while promising for
automating image quality evaluation, often fall short in real-world scenarios
due to their reliance on a generic quality standard applied uniformly across
diverse images. This one-size-fits-all approach overlooks the crucial
perceptual relationship between image content and quality, leading to a 'domain
shift' challenge where a single quality metric inadequately represents various
content types. Furthermore, BIQA techniques typically overlook the inherent
differences in the human visual system among different observers. In response
to these challenges, this paper introduces PICNIQ, an innovative pairwise
comparison framework designed to bypass the limitations of conventional BIQA by
emphasizing relative, rather than absolute, quality assessment. PICNIQ is
specifically designed to assess the quality differences between image pairs.
The proposed framework implements a carefully crafted deep learning
architecture, a specialized loss function, and a training strategy optimized
for sparse comparison settings. By employing psychometric scaling algorithms
like TrueSkill, PICNIQ transforms pairwise comparisons into
just-objectionable-difference (JOD) quality scores, offering a granular and
interpretable measure of image quality. We conduct our research using
comparison matrices from the PIQ23 dataset, which are published in this paper.
Our extensive experimental analysis showcases PICNIQ's broad applicability and
superior performance over existing models, highlighting its potential to set
new standards in the field of BIQA.