2025/05/04 by Vineesh Kumar Reddy Mondem, Mondem, Vineesh Kumar Reddy
Computer Science · Engineering · #94A08 #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.10 #I.4.0 #I.4.9 #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Infrared Target Detection Methodologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2505.02001
openalex publication_date 2025/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.