HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression

Junhao Cai, Taegun An, Chengjun Jin, Sung Il Choi, Juhyun Park, Changhee Joo

Abstract:

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 0.6 dB
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