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.

翻译：本文提出了一种基于Transformer的可变比特率图像压缩渐进式学习框架。我们的方法借助层自适应提示模块（LPM）覆盖了广泛的压缩率范围。受视觉提示调谐启发，我们在编码端和解码端分别使用LPM提取输入图像和隐藏特征的提示，将其作为附加信息输入预训练的基于Transformer的图像压缩模型中的Swin Transformer层，以影响注意力区域分配和比特分配，从而改变模型的目标压缩比。为确保网络更轻量化，我们整合了具有较少卷积层的提示网络。大量实验表明，与针对不同目标码率分别优化多个模型的方法相比，本方法在参数存储节省80%、数据集节省90%的情况下达到相同性能。同时，我们的模型在率失真性能上优于所有当前可变比特率图像方法，并接近从头训练的最优固定比特率图像压缩方法。