Perceptual Optimization

Perceptual optimization aims to improve the quality of machine-generated outputs (images, audio, text) by aligning them with human perception, rather than relying solely on objective metrics. Current research focuses on incorporating models of human visual and auditory systems (like Just Noticeable Difference thresholds) into optimization frameworks, often using techniques like maximum a posteriori estimation in latent spaces or novel loss functions tailored to perceptual characteristics. This approach leads to more natural and pleasing results in applications like image compression, speech enhancement, and large language model outputs, ultimately bridging the gap between computational efficiency and human experience.