  In recent years, large visual language models (LVLMs) have shown impressive
performance and promising generalization capability in multi-modal tasks, thus
replacing humans as receivers of visual information in various application
scenarios. In this paper, we pioneer to propose a variable bitrate image
compression framework consisting of a pre-editing module and an end-to-end
codec to achieve promising rate-accuracy performance for different LVLMs. In
particular, instead of optimizing an adaptive pre-editing network towards a
particular task or several representative tasks, we propose a new optimization
strategy tailored for LVLMs, which is designed based on the representation and
discrimination capability with token-level distortion and rank. The pre-editing
module and the variable bitrate end-to-end image codec are jointly trained by
the losses based on semantic tokens of the large model, which introduce
enhanced generalization capability for various data and tasks. {Experimental
results demonstrate that the proposed framework could efficiently achieve much
better rate-accuracy performance compared to the state-of-the-art coding
standard, Versatile Video Coding.} Meanwhile, experiments with multi-modal
tasks have revealed the robustness and generalization capability of the
proposed framework.
