Comparing Blind Image Quality Metrics for Reliable Image Assessment

Abstract

Reliable image quality assessment is essential not only in digital photography but also as a key metric for evaluating the performance of algorithms and models designed for image quality enhancement or generation. In recent years, a wide range of image quality assessment metrics, both traditional and learning-based, have been proposed, making it a challenge to select the appropriate method for a given task. This study presents a comparative analysis between five widely used traditional no-reference image quality assessment techniques and five machine learning-based approaches, evaluating their effectiveness in computing image quality scores. The evaluation is carried out comprehensively using a set of standard and advanced performance metrics. Furthermore, we analyze how characteristics of the training datasets, such as score distribution, influence model performance. The machine learning models considered vary significantly in architectural complexity, in terms of both the number of layers and parameters, and we investigate whether this variability has a considerable impact on prediction accuracy. The analysis also extends to non-photographic imagery, with a comparative evaluation of the methods on hyperspectral satellite image visualizations. For full transparency and reproducibility of the current study, all training parameters and hardware specifications are reported.