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

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.

💡 Research Summary

The paper addresses the emerging scenario of distributed multi‑stage image compression, where an image traverses several processing nodes—each possibly operating under different quality constraints—before reaching its final destination. Traditional single‑stage codecs (SSF) are designed for a one‑shot encode‑decode pipeline and cannot adapt after encoding. Progressive compression frameworks (PCF) generate scalable bitstreams that allow quality adaptation by truncating the stream, but they treat intermediate nodes as passive relays and cannot exploit the compute resources available at those nodes. Successive‑compression approaches (DRF) repeatedly decode and re‑encode the image at each hop, which leads to redundant pixel‑domain operations, high computational cost, and cumulative quality degradation.

To overcome these limitations, the authors propose the Hierarchical Cascade Framework (HCF). The key idea is to move the adaptation step from “after compression” to “during compression” by operating directly on the latent representation of the image rather than on the pixel domain. An image is first transformed by an analysis network gₐ into an unquantized latent ỹₛ. At each stage k (from source quality s down to destination quality d) the framework can apply either an inter‑node process or an intra‑node process:

• Inter‑node process (πₖ = 1): quantization Qₖ, entropy encoding Eₖ, entropy decoding Dₖ, followed by a transform module ϕᵢₙₜₑᵣₖ→ₖ₋₁ that is specifically trained for quantized latents.
• Intra‑node process (πₖ = 0): skips quantization and directly applies a transform ϕᵢₙₜᵣₐₖ→ₖ₋₁ designed for unquantized latents.

A binary policy vector π =