Pairwise Comparisons Are All You Need

• URL: http://arxiv.org/abs/2403.09746v2
• Date: Mon, 15 Jul 2024 18:19:48 GMT
• Title: Pairwise Comparisons Are All You Need
• Authors: Nicolas Chahine, Sira Ferradans, Jean Ponce,
• Abstract summary: Blind image quality assessment (BIQA) approaches often fall short in real-world scenarios due to their reliance on a generic quality standard applied uniformly across diverse images. This paper introduces PICNIQ, a pairwise comparison framework designed to bypass the limitations of conventional BIQA. By employing psychometric scaling algorithms, PICNIQ transforms pairwise comparisons into just-objectionable-difference (JOD) quality scores, offering a granular and interpretable measure of image quality.
• Score: 22.798716660911833
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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, a pairwise comparison framework designed to bypass the limitations of conventional BIQA by emphasizing relative, rather than absolute, quality assessment. PICNIQ is specifically designed to estimate the preference likelihood of quality between image pairs. By employing psychometric scaling algorithms, PICNIQ transforms pairwise comparisons into just-objectionable-difference (JOD) quality scores, offering a granular and interpretable measure of image quality. The proposed framework implements a deep learning architecture in combination with a specialized loss function, and a training strategy optimized for sparse pairwise comparison settings. 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 competitive performance, highlighting its potential to set new standards in the field of BIQA.

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