Learned image compression codecs have recently achieved impressive compression performances surpassing the most efficient image coding architectures. However, most approaches are trained to minimize rate and distortion which often leads to unsatisfactory visual results at low bitrates since perceptual metrics are not taken into account. In this paper, we show that conditional diffusion models can lead to promising results in the generative compression task when used as a decoder, and that, given a compressed representation, they allow creating new tradeoff points between distortion and perception at the decoder side based on the sampling method.

翻译：学习型图像压缩编解码器近期在压缩性能上取得了显著进展，超越了最高效的图像编码架构。然而，大多数方法在训练时仅优化码率和失真，未考虑感知指标，导致低比特率下的视觉质量不尽人意。本文证明，条件扩散模型作为解码器应用于生成式压缩任务时具有显著潜力；给定压缩表示后，这类模型能基于采样方法在解码端建立失真与感知之间的新型权衡点。