WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression

2025-03-23

AI Summary

Whole-slide images (WSIs) incur prohibitive storage costs due to their ultra-high resolution, and existing lossless compression methods fail because they cannot effectively model the intrinsic information irregularity of WSIs—specifically, the coexistence of local spectral heterogeneity and structural sparsity. This work identifies information irregularity as the fundamental cause of low compression ratios in WSIs and proposes the first WSI-specific lossless compression framework. It employs hierarchical bit-level coding to capture multi-scale structural regularities, adaptive dictionary modeling to characterize local spectral variations, and entropy-optimized re-representation to reduce overall entropy. Evaluated on gigapixel-scale WSI datasets, the method achieves an average compression ratio of 36× and a peak of 136×, substantially outperforming mainstream codecs including PNG, FLIF, and JPEG-LS. This establishes a new paradigm for efficient WSI storage and transmission.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides, WSIs require considerable data volume, which makes the storage and maintenance of WSI records costly and unsustainable. To this end, this work presents the first investigation of lossless compression of WSI images. Interestingly, we find that most existing compression methods fail to compress the WSI images effectively. Furthermore, our analysis reveals that the failure of existing compressors is mainly due to information irregularity in WSI images. To resolve this issue, we developed a simple yet effective lossless compressor called WISE, specifically designed for WSI images. WISE employs a hierarchical encoding strategy to extract effective bits, reducing the entropy of the image and then adopting a dictionary-based method to handle the irregular frequency patterns. Through extensive experiments, we show that WISE can effectively compress the gigapixel WSI images to 36 times on average and up to 136 times.

Problem

• Addresses high storage costs of gigapixel Whole-Slide Images (WSIs).
• Identifies inefficiency of existing lossless compression methods for WSIs.
• Proposes WISE framework to reduce entropy and compress WSIs effectively.

Innovation

• Hierarchical encoding strategy for bit extraction
• Dictionary-based method for irregular patterns
• Lossless compression for gigapixel WSI images