GAN-based image compression schemes have shown remarkable progress lately due
to their high perceptual quality at low bit rates. However, there are two main
issues, including 1) the reconstructed image perceptual degeneration in color,
texture, and structure as well as 2) the inaccurate entropy model. In this
paper, we present a novel GAN-based image compression approach with improved
rate-distortion optimization (RDO) process. To achieve this, we utilize the
DISTS and MS-SSIM metrics to measure perceptual degeneration in color, texture,
and structure. Besides, we absorb the discretized gaussian-laplacian-logistic
mixture model (GLLMM) for entropy modeling to improve the accuracy in
estimating the probability distributions of the latent representation. During
the evaluation process, instead of evaluating the perceptual quality of the
reconstructed image via IQA metrics, we directly conduct the Mean Opinion Score
(MOS) experiment among different codecs, which fully reflects the actual
perceptual results of humans. Experimental results demonstrate that the
proposed method outperforms the existing GAN-based methods and the
state-of-the-art hybrid codec (i.e., VVC).

本文提出了一种改进型的 GAN 图像压缩方法，通过使用 DISTS 和 MS-SSIM 指标对颜色、纹理和结构的感知退化度量以及吸收离散化的高斯 - 拉普拉斯 - 逻辑混合模型（GLLMM）来改善熵模型的准确性，并采用 MOS 实验直接评估重构图像的感知质量，实验结果表明该方法优于现有的 GAN 方法和 VVC 等最先进的混合编解码器。