Beyond Pixels: Assessing Image Quality through Semantic Information Loss

Despite the numerous approaches available in the state-of-the-art, the design of a full-reference image quality assessment metric aligned to human perception remains an open challenge. One possible reason is that most existing metrics fail to faithfully reproduce human judgments, as they disregard the global semantic structure of a scene. To fill this gap, we introduce a new method that evaluates image quality by explicitly modeling the preservation of semantic content. Segmentation masks are used to estimate semantic information loss, which is then integrated into the LPIPS framework, thus enabling the perceptual distance to account for semantic consistency. Experimental results show consistent improvements over the LPIPS baseline and competitive performance with respect to state-of-the-art methods, suggesting that semantic information loss is a useful cue for perceptual quality evaluation.