Hybrid Image Resolution Quality Metric (HIRQM):A Comprehensive Perceptual Image Quality Assessment Framework

TL;DR

The paper introduces HIRQM, a new comprehensive image quality metric that combines statistical, multi-scale, and deep learning methods, achieving high correlation with human perception across various distortions.

Contribution

It presents a unified perceptual image quality metric with a dynamic weighting mechanism, improving accuracy over traditional methods.

Findings

Achieves Pearson correlation of 0.92 and Spearman of 0.90 on TID2013 and LIVE datasets.

Outperforms traditional metrics in handling noise, blur, and compression artifacts.

Provides a flexible framework adaptable to different image distortions.

Abstract

Traditional image quality assessment metrics like Mean Squared Error and Structural Similarity Index often fail to reflect perceptual quality under complex distortions. We propose the Hybrid Image Resolution Quality Metric (HIRQM), integrating statistical, multi-scale, and deep learning-based methods for a comprehensive quality evaluation. HIRQM combines three components: Probability Density Function for local pixel distribution analysis, Multi-scale Feature Similarity for structural integrity across resolutions, and Hierarchical Deep Image Features using a pre-trained VGG16 network for semantic alignment with human perception. A dynamic weighting mechanism adapts component contributions based on image characteristics like brightness and variance, enhancing flexibility across distortion types. Our contributions include a unified metric and dynamic weighting for better perceptual alignment. Evaluated on TID2013 and LIVE datasets, HIRQM achieves Pearson and Spearman correlations of 0.92 and 0.90, outperforming traditional metrics. It excels in handling noise, blur, and compression artifacts, making it valuable for image processing applications like compression and restoration.