用低秩适配实现可变率图像压缩，大幅节省参数与训练成本。
Variable-Rate Deep Image Compression based on Low-Rank Adaptation by Progressive Learning
- 基于LoRA的渐进学习框架，通过可学习模块动态调整压缩率。
- 相比多模型方法，参数量减少99%，训练步骤节省97%。
- 适合资源受限场景下的高效图像压缩部署，如移动设备或车联网。
在数字时代，图像压缩对网络媒体、流媒体服务、高分辨率医学成像及车联网等应用至关重要，有助于实现高效的数据存储与传输。随着对高质量图像通信需求的增长，先进压缩技术愈发关键。尽管近期众多深度图像压缩（DIC）方法表现出色，但可变率压缩仍是未解难题。现有方法或采用多个网络以实现不同压缩率，或依赖单一模型，前者导致计算复杂度上升，后者性能下降。本文提出一种基于低秩适配（LoRA）的渐进学习方法，引入额外的LoRA率自适应模块（LoRAM）。通过重新参数化融合LoRA，推理阶段无额外计算开销。实验表明，相比多模型方案，本方法在保持竞争力性能的同时，参数存储减少99%，数据集规模降低90%，训练步数节省97%。
原文摘要 · Abstract (English)
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.
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