# Frozen vision encoder

Image compression, restoration and generation all need to measure how different two images look to a person, yet the most accurate perceptual distances are fitted to human judgments and lose accuracy when resolution changes. JLD turns the Jacobian of a frozen vision encoder's output with respect to its early features into one fixed metric tensor, fitted once from 100 unlabeled images in about 35 seconds with no human labels. Across TID2013, CSIQ, LIVE and KADID-10k it reaches a mean Spearman correlation of 0.926, above 0.915 for VSI and 0.900 for DISTS; on TID2013 its lens-term correlation falls only from 0.850 to 0.845 when resolution doubles from 256 to 512 pixels, where DISTS drops from 0.815 to 0.717.

Image compression, restoration and generation all need to measure how different two images look to a person, yet the most accurate perceptual distances are fitted to human judgments and lose accuracy when resolution changes. JLD turns the Jacobian of a frozen vision encoder's output with respect to its early features into one fixed metric tensor, fitted once from 100 unlabeled images in about 35 seconds with no human labels. Across TID2013, CSIQ, LIVE and KADID-10k it reaches a mean Spearman correlation of 0.926, above 0.915 for VSI and 0.900 for DISTS; on TID2013 its lens-term correlation falls only from 0.850 to 0.845 when resolution doubles from 256 to 512 pixels, where DISTS drops from 0.815 to 0.717.