[Submitted on 15 Jul 2026]
Title:MixCompress: Mixture of Experts for Variable Rate Learned Image Compression
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Abstract:Learned image compression (LIC) is bottlenecked by the need to store independent models for each rate-distortion operating point. Existing variable bit-rate (VBR) methods aim to reduce this overhead via dense parameter modulation, but forcing a shared backbone to approximate divergent mappings causes severe feature entanglement. Specifically, low-rate smoothing gradients inherently conflict with the preservation of high-frequency textural details, leading to sub-optimal performance. To resolve this, we propose MixCompress, a unified VBR framework based on sparse structural specialization. While sparsely gated Mixture-of-Experts (MoE) routing successfully mitigates gradient conflict, it operates on a fixed computational budget. To address the increased representational demands of higher bit-rates we introduce a Mixture-of-Depths (MoD) extension to dynamically scale model capacity. Combined with Conditional Auxiliary Transforms (CAT) for dynamic sub-band energy modulation, our hierarchical framework effectively dynamically scales capacity. Extensive evaluations demonstrate that MixCompress not only matches individually optimized single-rate baselines but can even surpass them, establishing a new Pareto frontier for computationally efficient image coding.
Comments: Accepted to ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.14334 [cs.CV]
(or arXiv:2607.14334v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.14334
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Calvin-Khang Ta [view email] [v1] Wed, 15 Jul 2026 19:57:15 UTC (1,242 KB)
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