Effective image compression using transformer and residual network for balanced handling of high and low-frequency information.

ABSTRACT: Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network,

SUBMITTER: Hu J 

PROVIDER: S-EPMC12494236 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature