Efficient Learned Image Compression without Entropy Coding
Entropy coding is widely used in typical learned image compression (LIC) that converts latents into a compact bitstream. However, entropy coding is typically sequential and becomes the coding latency bottleneck. To overcome it, we present Entropy-Coding Free Learned Image Compression (EF-LIC), a multi-rate framework that generates compact representation by removing statistical and correlation redundancy with low coding latency. First, we introduce unconstrained vector quantization and prove that its index distribution approaches the maximum-entropy bound, yielding minimal statistical redundancy. Second, we propose a context-conditioned autoregressive transform that directly reparameterize...
| Authors | Hao Cao, Wenqi Guo, Zhijin Qin et al. |
|---|---|
| Primary category | eess.IV |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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