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

2025-08-04

AI Summary

To address low computational resource utilization, cumulative distortion from sequential compression, and lack of post-encoding quality adaptability in distributed multi-stage image compression, this paper proposes the Hierarchical Cascaded Framework (HCF). HCF abandons conventional pixel-domain repetitive operations and instead performs cross-node cascaded transformations in the latent space. It introduces policy-driven quantization control and a differential-entropy-based edge quantization mechanism, enabling quality-adaptive compression without retraining. Leveraging end-to-end differentiable entropy estimation and multi-stage joint optimization, HCF achieves up to 97.8% reduction in FLOPs and 90.0% reduction in inference latency compared to sequential compression methods on the Kodak and CLIC datasets, with a 5.56% BD-Rate improvement. Against state-of-the-art progressive methods, it attains a 12.64% BD-Rate gain, significantly enhancing both rate-distortion performance and computational efficiency.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Data Mining & Knowledge Management: Data Compression

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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 system. 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.

Problem

• Improves distributed multi-stage image compression efficiency
• Reduces computational costs and quality loss
• Enhances rate-distortion performance and flexibility

Innovation

• Hierarchical Cascade Framework for latent-space transformations
• Policy-driven quantization optimizes rate-distortion trade-offs
• Edge quantization principle enhances efficiency and performance