In the digital age, image compression is crucial for numerous applications, including web media, streaming services, high-resolution medical imaging, and connected vehicle networks, enabling efficient data storage and transmission. With the increasing demand for high-quality image communication, the need for advanced compression techniques becomes increasingly critical. Numerous Deep Image Compression (DIC) techniques have recently been introduced, showing impressive performance compared to traditional standards. However, variable-rate image compression remains an unresolved issue. Specific DIC methods deploy multiple networks to attain different compression rates, whereas others use a single model, which often results in higher computational complexity and reduced performance. This work proposes a progressive learning approach for variable-rate image compression based on the parameter-efficient fine-tuning method, the Low-Rank Adaptation (LoRA). We introduce an additional LoRA Rate-Adaptive Module (LoRAM) in DIC methods. Due to the re-parameterized merging of LoRA, our proposed method does not introduce additional computational complexity during inference. Compared to methods utilizing multiple models, comprehensive experiments demonstrate that our approach achieves competitive performance, saving 99\% in parameter storage, 90% in datasets, and 97% in training steps.

翻译：在数字时代，图像压缩对于众多应用（包括网络媒体、流媒体服务、高分辨率医学成像和车联网）至关重要，能够实现高效的数据存储与传输。随着高质量图像通信需求的日益增长，先进压缩技术的需求变得愈发关键。近年来涌现出多种深度图像压缩技术，其性能相较于传统标准展现出显著优势。然而，变速率图像压缩仍是一个未解决的难题。特定深度图像压缩方法需部署多个网络以实现不同压缩率，而另一些方法虽采用单一模型却常导致更高的计算复杂度和性能下降。本文提出基于参数高效微调方法——低秩适应的渐进式学习框架，用于变速率图像压缩。我们在深度图像压缩方法中引入额外的低秩适应速率自适应模块。由于低秩适应的重参数化合并特性，本方法在推理过程中不引入额外计算开销。与采用多模型的方法相比，综合实验表明本方法在参数存储、数据集和训练步骤上分别节省99%、90%和97%的同时，实现了具有竞争力的性能。