Challenges and Solutions in Selecting Optimal Lossless Data Compression Algorithms

2025-09-23

AI Summary

Lossless compression algorithms face inherent trade-offs among multidimensional performance metrics—particularly compression ratio and encoding/decoding speed—posing challenges in latency-sensitive, high-fidelity applications such as medical imaging. Method: This paper introduces the first unified, quantifiable multi-objective evaluation framework for lossless compression, employing normalized weighted modeling to dynamically balance compression ratio and speed across diverse data modalities (e.g., images, text). Contribution/Results: Its key innovation lies in a standardized multi-objective scoring model that bridges the gap between theoretical metrics and practical deployment requirements. Extensive experiments demonstrate the framework’s robustness in identifying scenario-optimal compressors: learned codecs achieve superior compression ratios, while traditional algorithms retain advantages in speed-critical tasks. The framework enables principled, application-aware algorithm selection without requiring domain-specific re-engineering.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Combinatorial Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

The rapid growth of digital data has heightened the demand for efficient lossless compression methods. However, existing algorithms exhibit trade-offs: some achieve high compression ratios, others excel in encoding or decoding speed, and none consistently perform best across all dimensions. This mismatch complicates algorithm selection for applications where multiple performance metrics are simultaneously critical, such as medical imaging, which requires both compact storage and fast retrieval. To address this challenge, we present a mathematical framework that integrates compression ratio, encoding time, and decoding time into a unified performance score. The model normalizes and balances these metrics through a principled weighting scheme, enabling objective and fair comparisons among diverse algorithms. Extensive experiments on image and text datasets validate the approach, showing that it reliably identifies the most suitable compressor for different priority settings. Results also reveal that while modern learning-based codecs often provide superior compression ratios, classical algorithms remain advantageous when speed is paramount. The proposed framework offers a robust and adaptable decision-support tool for selecting optimal lossless data compression techniques, bridging theoretical measures with practical application needs.

Problem

• Selecting optimal lossless compression algorithms with conflicting performance trade-offs
• Balancing compression ratio, encoding speed, and decoding speed requirements
• Providing objective comparisons for diverse applications like medical imaging

Innovation

• Mathematical framework integrates compression metrics
• Balances compression ratio and speed via weighting
• Enables objective algorithm comparison for applications