Scaling Context for Unified Variable-Rate and Variable-Complexity Image Compression

Learned image compression (LIC) increasingly requires flexible rate–distortion–complexity (R–D–C) control for heterogeneous deployment. We introduce context scaling, a complementary scaling dimension that varies autoregressive context modeling within a fixed codec, rather than changing model capacity. By adjusting the context-modeling schedule, a single codec can jointly support variable rate and variable complexity while reusing the same transform output across complexity levels. Interestingly, we find that the benefit of context scaling is strongly input dependent: some images saturate with coarse context modeling, whereas others continue to benefit from finer-grained conditioning. Based on this observation, we introduce an adaptive context schedule that assigns finer context modeling to inputs with larger expected gains under a given complexity budget. Experiments on standard benchmarks show that context scaling provides effective R–D–C scalability, while adaptive scheduling consistently outperforms fixed schedules at matched average complexity. These results establish context modeling as a controllable complexity dimension orthogonal to model-capacity scaling, and reveal input-dependent context allocation as an effective mechanism for improving compression efficiency under constrained computation.