  Although recent generative image compression methods have demonstrated
impressive potential in optimizing the rate-distortion-perception trade-off,
they still face the critical challenge of flexible rate adaption to diverse
compression necessities and scenarios. To overcome this challenge, this paper
proposes a Controllable Generative Image Compression framework, Control-GIC,
the first capable of fine-grained bitrate adaption across a broad spectrum
while ensuring high-fidelity and generality compression. We base Control-GIC on
a VQGAN framework representing an image as a sequence of variable-length codes
(i.e. VQ-indices), which can be losslessly compressed and exhibits a direct
positive correlation with the bitrates. Therefore, drawing inspiration from the
classical coding principle, we naturally correlate the information density of
local image patches with their granular representations, to achieve dynamic
adjustment of the code quantity following different granularity decisions. This
implies we can flexibly determine a proper allocation of granularity for the
patches to acquire desirable compression rates. We further develop a
probabilistic conditional decoder that can trace back to historic encoded
multi-granularity representations according to transmitted codes, and then
reconstruct hierarchical granular features in the formalization of conditional
probability, enabling more informative aggregation to improve reconstruction
realism. Our experiments show that Control-GIC allows highly flexible and
controllable bitrate adaption and even once compression on an entire dataset to
fulfill constrained bitrate conditions. Experimental results demonstrate its
superior performance over recent state-of-the-art methods.
