Distributed multi-stage image compression -- where visual content traverses multiple processing nodes under varying quality requirements -- poses challenges. Progressive methods enable bitstream truncation but underutilize available compute resources; successive compression repeats costly pixel-domain operations and suffers cumulative quality loss and inefficiency; fixed-parameter models lack post-encoding flexibility. In this work, we developed the Hierarchical Cascade Framework (HCF) that achieves high rate-distortion performance and better computational efficiency through direct latent-space transformations across network nodes in distributed multi-stage image compression systems. Under HCF, we introduced policy-driven quantization control to optimize rate-distortion trade-offs, and established the edge quantization principle through differential entropy analysis. The configuration based on this principle demonstrates up to 0.6dB PSNR gains over other configurations. When comprehensively evaluated on the Kodak, CLIC, and CLIC2020-mobile datasets, HCF outperforms successive-compression methods by up to 5.56% BD-Rate in PSNR on CLIC, while saving up to 97.8% FLOPs, 96.5% GPU memory, and 90.0% execution time. It also outperforms state-of-the-art progressive compression methods by up to 12.64% BD-Rate on Kodak and enables retraining-free cross-quality adaptation with 7.13-10.87% BD-Rate reductions on CLIC2020-mobile.

翻译：分布式多级图像压缩——即视觉内容在多个处理节点间传输并需满足不同质量要求——带来了诸多挑战。渐进式方法支持码流截断，但未能充分利用可用的计算资源；逐级压缩重复了代价高昂的像素域操作，导致累积的质量损失和效率低下；固定参数模型则缺乏编码后的灵活性。本研究提出了分层级联框架（HCF），通过在分布式多级图像压缩系统中各网络节点间直接进行潜在空间变换，实现了高率失真性能和更优的计算效率。在HCF框架下，我们引入了策略驱动的量化控制以优化率失真权衡，并通过微分熵分析建立了边缘量化原理。基于该原理的配置相比其他配置在PSNR上最高可提升0.6dB。在Kodak、CLIC和CLIC2020-mobile数据集上的综合评估表明，HCF在CLIC数据集上以PSNR衡量的BD-Rate优于逐级压缩方法最高达5.56%，同时节省了最高97.8%的FLOPs、96.5%的GPU内存和90.0%的执行时间。此外，HCF在Kodak数据集上以BD-Rate衡量优于当前最先进的渐进式压缩方法最高达12.64%，并在CLIC2020-mobile数据集上实现了无需重新训练的多质量自适应，BD-Rate降低了7.13%至10.87%。