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

• URL: http://arxiv.org/abs/2605.02863v1
• Date: Mon, 04 May 2026 17:39:07 GMT
• Title: Pixel Perfect: Relational Image Quality Assessment with Spatially-Aware Distortions
• Abstract summary: Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS)<n>We overcome these limitations by shifting from absolute quality prediction to a relational and directional assessment.<n>A distortion prediction network is trained with an anti-symmetric objective to produce spatially-aware, disentangled maps.<n>A scoring network is trained via contrastive learning on ordinally ranked image sets to predict a relational quality score.
• Score: 12.998174707153844
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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