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Lossy image coding standards such as JPEG and MPEG have successfully achievedhigh compression rates for human consumption of multimedia data. However, withthe increasing prevalence of IoT devices, drones, and self-driving cars,machines rather than humans are processing a greater portion of captured visualcontent. Consequently, it is crucial to pursue an efficient compressedrepresentation that caters not only to human vision but also to imageprocessing and machine vision tasks. Drawing inspiration from the efficientcoding hypothesis in biological systems and the modeling of the sensory cortexin neural science, we repurpose the compressed latent representation toprioritize semantic relevance while preserving perceptual distance. Ourproposed method, Compressed Perceptual Image Patch Similarity (CPIPS), can bederived at a minimal cost from a learned neural codec and computedsignificantly faster than DNN-based perceptual metrics such as LPIPS and DISTS.

CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression