Learned Image Compression with Frequency Feature Interaction and Non-local Cross-similarity Prior

Recently learned image compression models have achieved better compression performance than traditional non-learning image compression standards. Those learned models usually utilize spatial self-attention and CNN to extract non-local and local features and generate the latent representation. However, previous methods adopt a linear layer to fuse non-local and local features and lack the flexibility to adaptively adjust feature weights and capture complex non-linear interactions between distinct feature representations. Additionally, how to more effectively compress the latent representation based on its channel similarity characteristics remains unexplored. To solve the above issues, we propose a novel image compression method with frequency feature interaction and non-local cross-similarity prior. More specifically, we extend the previous spatial self-attention module and alternately use spatial and channel self-attention modules to extract non-local spatial and channel features, respectively, and depth-wise convolution is utilized to extract local features. As local features focus on high-frequency detail information and non-local features concentrate on low-frequency structural information, we propose a frequency interaction module (FIM) that generates two weight maps to dynamically fuse non-local and local features. Moreover, we observe the non-local cross-similarity in different channels of the latent representation, which indicates that different channels share similar non-local semantic and structural information, but have distinct local detail information. So we design a dual transformer entropy model to emphasize non-local features and remove local features. Experiment results validate our method achieves promising compression performance on the Kodak, CLIC and Tecnick datasets.