Multi-View 3D Reconstruction From Video With Transformer

Image compression is a challenging work. However, few works consider using the property of image entropy to compress images. in this paper, we observe that there is a certain law between the entropy of feature maps in the field of image compression based on deep learning. That is, the subtraction of different level feature maps can obtain smaller information entropy. Smaller information entropy can bring lower uncertainty, thereby removing redundancy in the compression process, which can achieve better compression efficiency. Based on the observation above, we propose a framework based on information entropy reduction attention (IERA) for image compression to obtain better compression efficiency. Meanwhile, we design a IERA module to reduce the information entropy of feature maps during encoding and decoding. Extensive experiments demonstrate that our model outperforms many existing classical and learned image compression frameworks.