  Recently, the field of Image Coding for Machines (ICM) has garnered
heightened interest and significant advances thanks to the rapid progress of
learning-based techniques for image compression and analysis. Previous studies
often require training separate codecs to support various bitrate levels,
machine tasks, and networks, thus lacking both flexibility and practicality. To
address these challenges, we propose a rate-distortion-cognition controllable
versatile image compression, which method allows the users to adjust the
bitrate (i.e., Rate), image reconstruction quality (i.e., Distortion), and
machine task accuracy (i.e., Cognition) with a single neural model, achieving
ultra-controllability. Specifically, we first introduce a cognition-oriented
loss in the primary compression branch to train a codec for diverse machine
tasks. This branch attains variable bitrate by regulating quantization degree
through the latent code channels. To further enhance the quality of the
reconstructed images, we employ an auxiliary branch to supplement residual
information with a scalable bitstream. Ultimately, two branches use a `$\beta x
+ (1 - \beta) y$' interpolation strategy to achieve a balanced
cognition-distortion trade-off. Extensive experiments demonstrate that our
method yields satisfactory ICM performance and flexible
Rate-Distortion-Cognition controlling.
