GAN-based Image Compression with Improved RDO Process
Fanxin Xia Jian Jin Lili Meng Feng Ding Huaxiang Zhang
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).