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

通过在神经科学中建模感知皮层和生物系统中的高效编码假设，我们重新利用压缩潜在表示来优先考虑语义相关性，同时保留感知距离。我们提出的方法，压缩感知图像补丁相似度（CPIPS），可以从学习的神经编解码器以较低的成本进行计算，并且比基于深度神经网络的感知度量（如 LPIPS 和 DISTS）计算速度显著更快。