GroupedMixer: An Efficient Transformer-based Entropy Model with Group-wise Token-Mixers for Learned Image Compression

The proposed GroupedMixer is a novel transformer-based entropy model that employs group-wise autoregression and decomposes the global attention mechanism into more efficient inner-group and cross-group token-mixers, enabling faster coding speed and better compression performance compared to previous transformer-based methods.