In recent years, neural network-driven image compression (NIC) has gained
significant attention. Some works adopt deep generative models such as GANs and
diffusion models to enhance perceptual quality (realism). A critical obstacle
of these generative NIC methods is that each model is optimized for a single
bit rate. Consequently, multiple models are required to compress images to
different bit rates, which is impractical for real-world applications. To
tackle this issue, we propose a variable-rate generative NIC model.
Specifically, we explore several discriminator designs tailored for the
variable-rate approach and introduce a novel adversarial loss. Moreover, by
incorporating the newly proposed multi-realism technique, our method allows the
users to adjust the bit rate, distortion, and realism with a single model,
achieving ultra-controllability. Unlike existing variable-rate generative NIC
models, our method matches or surpasses the performance of state-of-the-art
single-rate generative NIC models while covering a wide range of bit rates
using just one model. Code will be available at this https URL

近年来，神经网络驱动的图像压缩（NIC）方法备受关注。本文提出了一种可变比率的生成式 NIC 模型，通过多种鉴别器设计和引入新的对抗损失，结合新提出的多真实感技术，用户能够通过单一模型灵活地调整比特率、失真和真实感，实现了极高的可控性并覆盖宽范围的比特率。与已有可变比率生成式 NIC 模型不同的是，本方法在只使用一个模型的情况下，达到或超过最先进的单比率生成式 NIC 模型的性能。