A Parametric Bi-Directional Curvature-Based Framework for Image Artifact Classification and Quantification
This provides a robust tool for automatically classifying and quantifying image artifacts like blur and noise, which is important for applications in image processing and computer vision, though it appears incremental as it builds on existing NR-IQA methods.
The paper tackles the problem of No-Reference Image Quality Assessment (NR-IQA) by developing a framework based on directional image curvature and Anisotropic Texture Richness (ATR), achieving Spearman correlations of approximately -0.93 for Gaussian blur and -0.95 for white noise on the LIVE dataset, and a complete system with R²=0.892 and RMSE=5.17 DMOS points.
This work presents a novel framework for No-Reference Image Quality Assessment (NR-IQA) founded on the analysis of directional image curvature. Within this framework, we define a measure of Anisotropic Texture Richness (ATR), which is computed at the pixel level using two tunable thresholds -- one permissive and one restrictive -- that quantify orthogonal texture suppression. When its parameters are optimized for a specific artifact, the resulting ATR score serves as a high-performance quality metric, achieving Spearman correlations with human perception of approximately -0.93 for Gaussian blur and -0.95 for white noise on the LIVE dataset. The primary contribution is a two-stage system that leverages the differential response of ATR to various distortions. First, the system utilizes the signature from two specialist ATR configurations to classify the primary artifact type (blur vs. noise) with over 97% accuracy. Second, following classification, it employs a dedicated regression model mapping the relevant ATR score to a quality rating to quantify the degradation. On a combined dataset, the complete system predicts human scores with a coefficient of determination (R2) of 0.892 and a Root Mean Square Error (RMSE) of 5.17 DMOS points. This error corresponds to just 7.4% of the dataset's total quality range, demonstrating high predictive accuracy. This establishes our framework as a robust, dual-purpose tool for the classification and subsequent quantification of image degradation.