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.

该研究提出了一种渐进学习范式，用于基于变速器的图像压缩。通过使用层自适应提示模块 (LPM) 提取输入图像和编码器端以及解码器端的隐藏特征的提示信息，并将其作为额外信息输入到预训练的基于变速器的图像压缩模型的 Svin Transformer 层中，从而改变了模型的关注区域和位数的分配，进而改变了模型的目标压缩比。通过与仅优化不同目标比率的多模型方法相比较，详尽实验证明了所提出的方法在参数存储上节约了 80% 的空间，节约了 90% 的数据集。同时，我们的模型在速率失真性能方面胜过了所有当前的可变比特率图像方法，并接近从头开始训练的最先进的固定比特率图像压缩方法。