Blind Quality Metric for Contrast-Distorted Images Based on Eigendecomposition of Color Histograms

Abstract

Although contrast is a major issue in overall quality assessment of an image, existing contrast evaluators with a reasonable performance are currently scarce. Here, we propose a learning-based blind/no-reference (NR) image quality assessment (IQA) model, a dubbed histogram eigen-feature-based contrast score (HEFCS) for evaluating image contrast. This research seeks for the inter-relationship between contrast degradation and relevant image histogram features. We introduce “eigen-histograms,” which are the eigenvectors of the set of image patches' histograms. We found that the randomness of image eigen-histograms and the amplitude of corresponding eigenvalues can reliably reflect the changes in image contrast. Employing these characteristics leads to contrast-aware HEF vectors, which are used to compute the contrast score through a prediction model trained using support vector regression. Extensive analysis and cross validation are performed with five contrast relevant image databases, and the HEFCS performance results are compared with a collection of full-reference (FR), reduced-reference (RR), and NR measures. Despite its simplicity and low computational complexity, the HEFCS performs better than all competing NR-IQA models and also stands among the three best performers of FR and RR models.