Just-noticeable differences (JNDs) act as a perceptual filter for AI systems, defining the threshold below which humans cannot detect change. In generative media—where perfect physical accuracy is computationally prohibitive—JNDs reveal where precision matters: small errors in salient regions like faces can drive strong perceptual shifts, while larger errors in background detail or minor latency fluctuations often go unnoticed. Modeling these spatial and temporal thresholds allows systems to allocate compute to perceptually meaningful improvements.