OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data

• URL: http://arxiv.org/abs/2602.22286v2
• Date: Mon, 02 Mar 2026 12:21:30 GMT
• Title: OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data
• Abstract summary: Lossless compression is essential for efficient data storage and transmission.<n>We propose textbf OmniZip, a unified and lightweight compressor for multi-modal data.<n>Built on a lightweight backbone, OmniZip incorporates three key components to enable efficient multi-modal compression.
• Score: 20.09409492679677
• License: http://creativecommons.org/licenses/by/4.0/
• 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 design that further enhances the model's nonlinear representation flexibility. A reparameterization training strategy is used to enhance model capacity. OmniZip outperforms or matches other state-of-the-art compressors on multiple modalities, achieving 42\%, 57\%, 62\% and 42\%, 53\% higher compression efficiency than gzip on CLIC-M, TouchandGo, enwik9, LibriSpeech, and WikiSQL datasets, respectively. It also supports near real-time inference on resource-constrained edge devices, reaching about 1MB/s on MacBook CPUs and iPhone NPUs. Our code is released at https://github.com/adminasmi/OmniZip-CVPR2026.

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