Learned Image Compression using Transformer and Residual Network for Effective Handling of High-and Low-Frequency Information

Jianhua Hu, Guixiang Luo, Xiangfei Feng, Zhanjiang Yuan, Jiahui Yang, Wei Nie

Abstract: The paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network in Figure 1 . Our 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, intricately designed as a high-low-frequency mixer. This dual network can preserve both high and low-frequency features during image compression.