Efficient Learned Data Compression via Dual-Stream Feature Decoupling
While Learned Data Compression (LDC) has achieved superior compression ratios, balancing precise probability modeling with system efficiency remains challenging. Crucially, uniform single-stream architectures struggle to simultaneously capture micro-syntactic and macro-semantic features, necessitating deep serial stacking that exacerbates latency. Compounding this, heterogeneous systems are constrained by device speed mismatches, where throughput is capped by Amdahl's Law due to serial processing. To this end, we propose a Dual-Stream Multi-Scale Decoupler that disentangles local and global contexts to replace deep serial processing with shallow parallel streams, and incorporate a Hierarc...
| Authors | Huidong Ma, Xinyan Shi, Hui Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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