Pixel Perfect: Relational Image Quality Assessment with Spatially-Aware Distortions

2026-05-04

AI Summary

Traditional image quality assessment relies heavily on subjective mean opinion scores, which incur high annotation costs and lack local interpretability. To address these limitations, this work proposes a label-free, relational, and directional image quality assessment method. It leverages a self-supervised synthetic distortion engine to generate training data and integrates a spatially aware, disentangled distortion map prediction mechanism with a contrastive learning–based relational scoring network. The proposed approach accurately identifies distortion type, intensity, and orientation, yielding fine-grained and interpretable quality predictions. Furthermore, it enables targeted optimization of image processing algorithms by providing actionable, spatially localized quality feedback.

Abstract

Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS), which are resource-intensive to collect and fail to provide interpretable, localized feedback on specific image distortions. We overcome these limitations by shifting from absolute quality prediction to a relational and directional assessment. Our approach utilizes a self-supervised synthetic distortion engine to generate training data, eliminating the need for manual annotation. A distortion prediction network is trained with an anti-symmetric objective to produce spatially-aware, disentangled maps that identify the type, intensity, and direction of distortions relative to a reference image. Subsequently, a scoring network is trained via contrastive learning on ordinally ranked image sets to predict a relational quality score. Our method provides a more granular and interpretable approach to IQA for the targeted optimization of image processing algorithms without requiring any human-labeled quality scores.

Problem

• image quality assessment
• mean opinion score
• spatially-aware distortions
• interpretable feedback
• localized distortion analysis

Innovation

• relational image quality assessment
• spatially-aware distortions
• self-supervised synthetic distortion
• disentangled distortion maps
• contrastive learning