Where Should the Compute Go? Practical Neural Image Compression across Budgets

Learned image compression has made substantial progress, yet its high computational cost remains a major barrier to practical deployment. Under tight complexity budgets, effective design requires not only reducing model complexity but also allocating computation across components. Taking edge-device complexity as the starting point, we present a systematic study of learned image compression under constrained budgets. Concretely, we first build a unified lightweight architecture and analyze computation allocation between the transform modules and the entropy model, then perform local structure search by independently optimizing and composing sub-modules, and finally construct a scalable model family via gradual expansion, with compact variants further improved by energy-based hierarchical pruning. The key aspect of our study is a systematic design procedure rather than any single network: under the unified architecture we establish, each specified complexity budget yields a configuration that consistently outperforms comparable low-complexity baselines across the evaluated constraints. As a representative result, with VTM-24.0 as the common anchor, our model achieves a 24.82% BD-rate reduction on Kodak relative to Shallow-NTC, a representative low-complexity decoding baseline, while reducing encoding and decoding complexity by about 84% and 13%, respectively.