ASRIC: Adaptive Sparse Routing for Learned Image Compression

Learned image codecs often allocate transform computation and entropy-model capacity uniformly across heterogeneous content. As such, low-complexity regions incur the same dense processing as structurally complex regions, while a fixed pool of entropy-model features accommodates diverse conditional latent distributions. This allocation can incur redundant computation on unnecessary transform updates and limit the specialization of probability estimates to local context, resulting in limited compression performance under a fixed computational budget. In this paper, we propose Adaptive Sparse Routing for Learned Image Compression (ASRIC) to match transform computation and entropy-model capacity to content through two complementary mechanisms. Within the transforms, Spatially Adaptive Mamba blocks are introduced to select spatial tokens and scan directions for recurrent processing based on the Mamba module, while coordinate-dependent state transitions preserve the geometric separation between selected tokens. For entropy modeling, decoder-available context selects Top-K prototype banks, providing specialized statistical features for each prediction without transmitting bank indices. Experimentally, ASRIC achieves BD-Rate reductions of 19.15%, 18.67%, and 20.37% relative to VTM-9.1 on Kodak, CLIC, and Tecnick, respectively. Compared with CMIC, the strongest learned baseline in our comparison, ASRIC improves the VTM-referenced BD-Rate by 0.94, 0.81, and 0.32 percentage points across the three benchmarks, while requiring 4.2% fewer FLOPs and reducing encoding and decoding time by 5.6% and 16.3%, respectively.