Channel-Level Mask Guidance for Slimmable Image Compression
Learned image compression (LIC) has achieved promising rate-distortion performance, but most methods lack scalability for diverse platforms with varying computational and storage constraints. Existing slimmable LIC methods achieve flexible complexity via consecutive-channel activation but suffer from two limitations: forced consecutive-channel selection degrades narrow-model performance at low bitrates, and the lack of stable variable-rate control limits adaptability to varying bandwidth. We propose a channel-level mask-guided slimmable image compression method. A learnable channel-wise mask with sparsity-promoting regularization enables non-contiguous top-w channel selection, letting nar...
| Authors | Chen Chen, Chenggang Wu, Yongbiao Chen et al. |
|---|---|
| Primary category | eess.IV |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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