Learning-Based Conditional Image Compression

Tianma Shen, Wen-Hsiao Peng, Huang-Chia Shih, Ying Liu

Abstract: In recent years, deep learning-based image compression has achieved significant success. Most schemes adopt an end-to-end trained compression network with a specifically designed entropy model. Inspired by recent advances in conditional video coding, in this work, we propose a novel transformer-based conditional coding paradigm for learned image compression. Our approach first compresses a low-resolution version of the target image and up-scales the decoded image using an off-the-shelf super-resolution model. The super-resolved image then serves as the condition to compress and decompress the target high-resolution image. Experiments demonstrate the superior rate-distortion performance of our approach compared to existing methods.

External IDs:dblp:conf/iscas/ShenPSL24