SWA-IC: Multirate Learned Image Compression with Sliding Window Attention

Learned image compression has surpassed conventional codecs in rate–distortion performance, but many leading methods require a separately trained model for each operating point. Supporting multiple rates therefore increases training and storage requirements and complicates deployment. We present SWA-IC, a single image compression model supporting 128 fine-grained operating points with state-of-the-art rate–distortion performance. We extend sliding window attention from autoregressive context modeling to the analysis and synthesis transforms, establishing a common SWA-based design for feature transformation and spatial context modeling. We give the synthesis transform direct access to context features for image reconstruction and equip SWA with learned attention sinks to suppress uninformative contributions. Growing channel groups and generalized Gaussian entropy modeling strengthen the parallel context model, while channel-wise gains control rate with shared network weights across all operating points. On Kodak, Tecnick, and CLIC, SWA-IC achieves BD-rate savings of $18.44$, $24.86$, and $18.53$, respectively, relative to VTM 23.10, obtaining the lowest BD-rate among the evaluated single-rate and multirate codecs. At medium and large tested resolutions, it also matches or improves on the decoding latency of previous state-of-the-art image codecs.