Structural-Equation-Modeling-Based Indicator Systems for Image Quality Assessment

Recent advancements in image denoising algorithms have significantly improved visual performance. However, they also introduce new challenges for image quality assessment (IQA) indicators to provide evaluations that align with human visual perception. To address the limitations of current single-indicator methods, we propose a comprehensive IQA framework that integrates multiple indicators to achieve a holistic assessment of image quality. We first develop a large-scale denoised image dataset to show the diversity of distortions. Then, we employ structural equation modeling to establish correlations among three fundamental aspects of image quality, i.e., structural similarity, information loss, and perceptual gain. Through a series of regressions and iterative refinements, we eliminate indicators with low accuracy and high redundancy, thus resulting in a robust and optimal indicator system. Finally, we systematically validate the reliability and effectiveness of the proposed system through statistical analysis and evaluate its performance across three key tasks, i.e., image quality prediction, IQA indicator comparison, and denoising algorithm optimization. Experimental results demonstrate that the proposed system not only offers highly reliable and valid assessments but also provides valuable insights for the analysis and application of IQA indicators.