  In this paper, we propose a progressive learning paradigm for
transformer-based variable-rate image compression. Our approach covers a wide
range of compression rates with the assistance of the Layer-adaptive Prompt
Module (LPM). Inspired by visual prompt tuning, we use LPM to extract prompts
for input images and hidden features at the encoder side and decoder side,
respectively, which are fed as additional information into the Swin Transformer
layer of a pre-trained transformer-based image compression model to affect the
allocation of attention region and the bits, which in turn changes the target
compression ratio of the model. To ensure the network is more lightweight, we
involves the integration of prompt networks with less convolutional layers.
Exhaustive experiments show that compared to methods based on multiple models,
which are optimized separately for different target rates, the proposed method
arrives at the same performance with 80% savings in parameter storage and 90%
savings in datasets. Meanwhile, our model outperforms all current variable
bitrate image methods in terms of rate-distortion performance and approaches
the state-of-the-art fixed bitrate image compression methods trained from
scratch.
