CI-ICM: Channel Importance-driven Learned Image Coding for Machines

• URL: http://arxiv.org/abs/2604.05347v1
• Date: Tue, 07 Apr 2026 02:36:47 GMT
• Title: CI-ICM: Channel Importance-driven Learned Image Coding for Machines
• Abstract summary: We propose a Channel Importance-driven learned Image Coding for Machines (CI-ICM) to maximize the performance of machine vision tasks.<n>The proposed CI-ICM achieves BD-mAP@50:95 gains of 16.25%$ in object detection and 13.72%$ in instance segmentation over the established baseline.
• Score: 56.1188469769241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Second, to properly allocate bitrate among feature channels, we propose a Feature Channel Grouping and Scaling (FCGS) module that non-uniformly groups the feature channels based on their importance and adjusts the dynamic range of each group. Based on FCGS, we further propose a Channel Importance-based Context (CI-CTX) module to allocate bits among feature groups and to preserve higher fidelity in critical channels. Third, to adapt to multiple machine tasks, we propose a Task-Specific Channel Adaptation (TSCA) module to adaptively enhance features for multiple downstream machine tasks. Experimental results on the COCO2017 dataset show that the proposed CI-ICM achieves BD-mAP@50:95 gains of 16.25$\%$ in object detection and 13.72$\%$ in instance segmentation over the established baseline codec. Ablation studies validate the effectiveness of each contribution, and computation complexity analysis reveals the practicability of the CI-ICM. This work establishes feature channel optimization for machine vision-centric compression, bridging the gap between image coding and machine perception.

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