AI RESEARCH
CI-ICM: Channel Importance-driven Learned Image Coding for Machines
arXiv CS.CV
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ArXi:2604.05347v1 Announce Type: cross Traditional human vision-centric image compression methods are suboptimal for machine vision centric compression due to different visual properties and feature characteristics. To address this problem, we propose a Channel Importance-driven learned Image Coding for Machines (CI-ICM), aiming to maximize the performance of machine vision tasks at a given bitrate constraint. First, we propose a Channel Importance Generation (CIG) module to quantify channel importance in machine vision and develop a channel order loss to rank channels in descending order.