Perceptual Image Patch Similarity

Perceptual image patch similarity focuses on developing computational methods that accurately assess the similarity between image patches, mirroring human visual perception. Current research emphasizes improving the robustness and efficiency of these methods, particularly through the development of learned perceptual metrics like LPIPS and its variants, which leverage deep neural networks to capture complex visual features. These advancements are crucial for various applications, including image compression, where efficient representations that preserve perceptual quality are essential for both human and machine vision tasks, and for improving the reliability of image similarity assessments in the face of adversarial attacks. The ultimate goal is to create metrics that reliably and efficiently quantify visual similarity, aligning closely with human judgment.