Perceptual Metric

Perceptual metrics aim to quantify the similarity between signals (images, audio, etc.) as perceived by humans, offering objective alternatives to subjective evaluations. Current research focuses on improving the accuracy and robustness of these metrics, particularly by leveraging deep learning architectures like Vision Transformers (ViTs) and exploring self-supervised learning methods to reduce reliance on large labeled datasets. These advancements are crucial for various applications, including image and audio quality assessment, medical image analysis, and the development of more human-centered autonomous systems, by providing more reliable and efficient evaluation tools.