Controllable Distortion-Perception Tradeoff Through Latent Diffusion for Neural Image Compression

Neural image compression often faces a challenging trade-off among rate, distortion and perception. While most existing methods typically focus on either achieving high pixel-level fidelity or optimizing for perceptual metrics, we propose a novel approach that simultaneously addresses both aspects for a fixed neural image codec. Specifically, we introduce a plug-and-play module at the decoder side that leverages a latent diffusion process to transform the decoded features, enhancing either low distortion or high perceptual quality without altering the original image compression codec. Our approach facilitates fusion of original and transformed features without additional training, enabling users to flexibly adjust the balance between distortion and perception during inference. Extensive experimental results demonstrate that our method significantly enhances the pretrained codecs with a wide, adjustable distortion-perception range while maintaining their original compression capabilities. For instance, we can achieve more than 150% improvement in LPIPS-BDRate without sacrificing more than 1 dB in PSNR.

Authors

• Chuqin Zhou Shanghai Jiao Tong University
• Guo Lu Shanghai Jiao Tong University
• Jiangchuan Li Shanghai Jiao Tong University
• Xiangyu Chen Institute of Artificial Intelligence (TeleAI), China Telecom
• Zhengxue Cheng Shanghai Jiao Tong University
• Li Song Shanghai Jiao Tong University
• Wenjun Zhang Shanghai Jiao Tong University

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