Generative models have significantly advanced generative AI, particularly in image and video generation. Recognizing their potential, researchers have begun exploring their application in image compression. However, existing methods face two primary challenges: limited performance improvement and high model complexity. In this paper, to address these two challenges, we propose a perceptual image compression solution by introducing a conditional diffusion model. Given that compression performance heavily depends on the decoder’s generative capability, we base our decoder on the diffusion transformer architecture. To address the model complexity problem, we implement the diffusion transformer architecture with Swin transformer. Equipped with enhanced generative capability, we further augment the decoder with informative features using a multi-scale feature fusion module. Experimental results …
本文提出一种基于条件扩散模型的感知图像压缩方法，旨在解决现有生成式压缩方法性能提升有限和模型复杂度高的问题。方法采用扩散Transformer作为解码器，并利用Swin Transformer实现高效架构，以增强生成能力；同时引入多尺度特征融合模块，为解码器提供更丰富的信息特征。实验结果表明该方法在感知图像压缩任务上取得了优越的性能。