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    Perceptual and Structural Evaluation of Super-Resolution Methods
    (Institute of Electrical and Electronics Engineers Inc., 2025) Metin, Ahmet; Kayipmaz, Yusuf
    This paper examines the underlying reasons for metric performance differences among resolution enhancement methods, analyzing why deep learning-based models, while optimizing perceptual realism, often yield lower scores in traditional structural metrics. The study explores the balance between structural fidelity and perceptual quality, detailing the factors that influence this trade-off and discussing the contextual appropriateness of various metrics. While LPIPS emerges as an effective tool for evaluating perceptual realism, SSIM and FSIM remain important in tasks where structural integrity is critical. These findings highlight the importance of carefully selecting evaluation metrics in accordance with the objectives and constraints of the target application. © 2025 IEEE.