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[2602.22286] OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data
Summary
OmniZip introduces a unified and lightweight lossless compressor designed for multi-modal data, enhancing compression efficiency across various data types while maintaining real-time performance on edge devices.
Why It Matters
As data continues to grow in complexity and volume, efficient compression methods are critical for storage and transmission. OmniZip's approach addresses the limitations of existing single-modality compressors, providing a versatile solution that can handle diverse data formats. This innovation is particularly relevant for applications in machine learning and data science, where multi-modal data is increasingly common.
Key Takeaways
- OmniZip achieves higher compression efficiency than traditional methods like gzip across multiple datasets.
- The model incorporates a modality-unified tokenizer and a flexible context learning mechanism for effective multi-modal data handling.
- Designed for resource-constrained environments, OmniZip supports near real-time inference on devices like MacBooks and iPhones.
Computer Science > Machine Learning arXiv:2602.22286 (cs) [Submitted on 25 Feb 2026] Title:OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data Authors:Yan Zhao, Zhengxue Cheng, Junxuan Zhang, Dajiang Zhou, Qunshan Gu, Qi Wang, Li Song View a PDF of the paper titled OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data, by Yan Zhao and 6 other authors View PDF HTML (experimental) Abstract:Lossless compression is essential for efficient data storage and transmission. Although learning-based lossless compressors achieve strong results, most of them are designed for a single modality, leading to redundant compressor deployments in multi-modal settings. Designing a unified multi-modal compressor is critical yet challenging, as different data types vary largely in format, dimension, and statistics. Multi-modal large language models offer a promising resolution but remain too complex for practical use. Thus, we propose \textbf{OmniZip}, \textbf{a unified and lightweight lossless compressor for multi-modal data (like image, text, speech, tactile, database, and gene sequence)}. Built on a lightweight backbone, OmniZip incorporates three key components to enable efficient multi-modal lossless compression: a modality-unified tokenizer that reversibly transforms diverse data into tokens, a modality-routing context learning mechanism that enables flexible multi-modal context modeling, and a modality-routing feedforward des...
