ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation
Recent progress in learning-based image compression has demonstrated that end-to-end optimization can substantially outperform traditional codecs by jointly learning compact latent representations and probabilistic entropy models. However, many existing approaches achieve high rate-distortion efficiency at the expense of increased computational cost and limited parallelism. This paper presents ARCHE - Autoregressive Residual Compression with Hyperprior and Excitation, an end-to-end learned image compression framework that balances modeling accuracy and computational efficiency. The proposed architecture unifies hierarchical, spatial, and channel-based priors within a single probabilistic ...
| Authors | Sofia Iliopoulou, Dimitris Ampeliotis, Athanassios Skodras |
|---|---|
| Primary category | eess.IV |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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