A new Image Similarity Metric for a Perceptual and Transparent Geometric and Chromatic Assessment

2026-01-27

AI Summary

This work proposes a novel, interpretable perceptual similarity metric that jointly models texture and chromatic distortions—addressing the limited perceptual consistency of existing image similarity measures under complex distortions involving both texture and color. The method quantifies texture differences using Earth Mover’s Distance and computes chromatic discrepancies in the perceptually uniform Oklab color space. By integrating these components, the approach not only achieves superior perceptual alignment but also offers visual interpretability to enhance evaluation transparency. Evaluated on the Berkeley-Adobe dataset featuring non-traditional distortions, the proposed metric significantly outperforms state-of-the-art methods, demonstrating particularly robust performance and higher perceptual consistency in scenarios involving shape-related distortions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements

Abstract

In the literature, several studies have shown that state-of-the-art image similarity metrics are not perceptual metrics; moreover, they have difficulty evaluating images, especially when texture distortion is also present. In this work, we propose a new perceptual metric composed of two terms. The first term evaluates the dissimilarity between the textures of two images using Earth Mover's Distance. The second term evaluates the chromatic dissimilarity between two images in the Oklab perceptual color space. We evaluated the performance of our metric on a non-traditional dataset, called Berkeley-Adobe Perceptual Patch Similarity, which contains a wide range of complex distortions in shapes and colors. We have shown that our metric outperforms the state of the art, especially when images contain shape distortions, confirming also its greater perceptiveness. Furthermore, although deep black-box metrics could be very accurate, they only provide similarity scores between two images, without explaining their main differences and similarities. Our metric, on the other hand, provides visual explanations to support the calculated score, making the similarity assessment transparent and justified.

Problem

• image similarity
• perceptual metric
• texture distortion
• chromatic assessment
• explainability

Innovation

• Perceptual Image Similarity
• Earth Mover's Distance
• Oklab Color Space
• Transparent Metric
• Texture and Chromatic Assessment