Lightweight Image Quality Prediction Guided by Perceptual Ranking Feedback

Abstract

Automatic Image Quality Assessment (IQA) remains a difficult challenge due to the complexity of mimicking the Human Visual System (HVS) and the limitations of traditional objective Image Quality Metrics (IQM). Existing learnable methods often involve high computational costs and fail to adequately capture the nuanced perceptual characteristics of the HVS, including the human ability to rank image quality and human sensitivity to differences in areas with high-frequency. In this study, we propose an effective approach that addresses these challenges by incorporating the characteristics of HVS and the perceptual classification into a lightweight IQM framework based on the transformer architecture. This allows our method to capture long-range dependencies effectively. Our approach leverages Objective Error Maps (OEMs) to enhance sensitivity to visual errors and employs a ranking module as an objective function, providing feedback on the perceptual quality at the feature level. Experimental results demonstrate that our approach not only achieves competitive performance compared to state-of-the-art IQMs but also significantly reduces computational complexity.