MixCompress: Mixture of Experts for Variable Rate Learned Image Compression

Learned image compression (LIC) is bottlenecked by theneed to store independent models for each rate-distortion operating point.Existing variable bit-rate (VBR) methods aim to reduce this overhead viadense parameter modulation, but forcing a shared backbone to approx-imate divergent mappings causes severe feature entanglement. Specifi-cally, low-rate smoothing gradients inherently conflict with the preser-vation of high-frequency textural details, leading to sub-optimal perfor-mance. To resolve this, we propose MixCompress, a unified VBR frame-work based on sparse structural specialization. While sparsely gatedMixture-of-Experts (MoE) routing successfully mitigates gradient con-flict, it operates on a fixed computational budget. To address the in-creased representational demands of higher bit-rates we introduce a Mixture-of-Depths (MoD) extension to dynamically scale model capacity. Com-bined with Conditional Auxiliary Transforms (CAT) for dynamic sub-band energy modulation, our hierarchical framework effectively dynam-ically scales capacity. Extensive evaluations demonstrate that MixCom-press not only matches individually optimized single-rate baselines butcan even surpass them, establishing a new Pareto frontier for computa-tionally efficient image coding.