  Image Coding for Machines (ICM) aims to compress images for AI tasks analysis
rather than meeting human perception. Learning a kind of feature that is both
general (for AI tasks) and compact (for compression) is pivotal for its
success. In this paper, we attempt to develop an ICM framework by learning
universal features while also considering compression. We name such features as
omnipotent features and the corresponding framework as Omni-ICM. Considering
self-supervised learning (SSL) improves feature generalization, we integrate it
with the compression task into the Omni-ICM framework to learn omnipotent
features. However, it is non-trivial to coordinate semantics modeling in SSL
and redundancy removing in compression, so we design a novel information
filtering (IF) module between them by co-optimization of instance
distinguishment and entropy minimization to adaptively drop information that is
weakly related to AI tasks (e.g., some texture redundancy). Different from
previous task-specific solutions, Omni-ICM could directly support AI tasks
analysis based on the learned omnipotent features without joint training or
extra transformation. Albeit simple and intuitive, Omni-ICM significantly
outperforms existing traditional and learning-based codecs on multiple
fundamental vision tasks.
