BACF-Net: An Attention-Convolution Fusion Architecture for Learned Image Compression

In the era of big data, efficient image compression is essential for managing the rapid growth of visual content. While traditional codecs have advanced significantly, their dependence on handcrafted techniques limits their effectiveness. The emergence of deep learning has transformed image compression by enabling end-to-end optimization. However, existing learned methods typically utilize convolutional neural networks for local modeling or transformers for capturing long-range dependencies, indicating potential areas for enhancement. We introduce a novel hybrid hyperprior-based architecture that combines the advantages of residual CNNs and transformers to improve rate-distortion performance in learned image compression. Additionally, our proposed Bifurcated Attention-Convolution Fusion (BACF) block employs a parallel configuration of an enhanced residual CNN with split attention alongside mixed transformer variants for multi-axis attention and shifted window-based attention. This design allows the network to effectively process and integrate both local details and high-level semantic information. Extensive experiments on the Kodak, CLIC, and Tecnick datasets show that our proposed method achieves competitive rate-distortion performance.