AFEC: Adaptive Feature Extraction Modules for Learned Image Compression

With the rapid development of various multimedia applications, research on image compression technology has become particularly important. Learning-based compression methods have developed rapidly and achieved excellent rate-distortion performance. Most existing researches have focused on designing a better entropy model to facilitate the probability estimation without attaching importance to how to extract features from images more effectively. However, information extracted by image compression networks is often not realistic and complete enough, especially when the fixed-shape receptive field of the compression network crosses the texture boundary of an image. In this paper, we propose to extract high-fidelity image features adaptively with local textures as the basic unit, which significantly improves the quality of the extracted information and enhances the compactness of the latent representation of the image. Besides, a cross-information-fusion gate is proposed to fuse the two features extracted from the adaptive image feature extraction branch and the main compression branch for reducing spatial redundancy in the latent representation. Experimental results demonstrate our proposed method achieves superior performance compared to existing learned image compression methods and traditional codecs and produces visually pleasing reconstructed images with high-fidelity details.