COMPREHENSIVE EVALUATION OF METRICS FOR IMAGE RESEMBLANCE

Marcel Prodan (), Giorgiana Violeta Vlasceanu () and Costin-Anton Boiangiu ()
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Costin-Anton Boiangiu: University Politehnica of Bucharest, Romania

Journal of Information Systems & Operations Management, 2023, vol. 17, issue 1, 161-185

Abstract: In order to measure image similarity in the field of Computer Science, this study will analyze the main metrics in-depth. Image resemblance metrics play a significant role in various domains, including but not limited to, digital image processing, computer vision, and machine learning. This study embarks on a journey through various pixel-based and structural similarity metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Multi- Scale Structural Similarity Index (MS-SSIM), and other advanced metrics like Feature Similarity Index (FSIM), Universal Quality Index (UQI), and Visual Information Fidelity (VIF). A comparative analysis of these metrics is conducted, shedding light on their specific pros and cons and their applicability in different contexts. The paper also addresses the importance and role of these metrics in the evolving field of deep learning. Lastly, we discuss the current challenges, and limitations of these metrics, and envision the future scope and advancements in image resemblance metrics.

Date: 2023
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