Efficient learned image compression with dual-route attention based on state space modeling

Efficient image compression is critical for real-time visual transmission, yet learning-based methods often suffer from high computational complexity. This paper proposes an efficient framework based on a State Space Model (SSM), which introduces a refined context modeling mechanism in the latent representation stage to enable long-range dependency modeling with linear computational complexity.We introduce a Dual-Route Attention (DRA) mechanism for channel space entropy modeling, which adaptively aggregates latent features through content-aware routing to minimize redundancy. Unlike existing methods that use SSM as generic backbones, our approach explicitly tailors SSM for entropy-oriented context modeling.Experimental results demonstrate a superior balance between rate-distortion performance and efficiency. Compared to state-of-the-art hybrid and Transformer-based architectures, our method reduces encoding/decoding time by up to 60-70% while improving BD-rate by 1.57%–2.96%. Furthermore, it outperforms existing SSM-based baselines by 4.63% in BD-rate under similar complexity, validating its effectiveness for high-quality, low-latency image compression.