Recent advances in generative compression methods have demonstrated
remarkable progress in enhancing the perceptual quality of compressed data,
especially in scenarios with low bitrates. Nevertheless, their efficacy and
applicability in achieving extreme compression ratios ($<0.1$ bpp) still remain
constrained. In this work, we propose a simple yet effective coding framework
by introducing vector quantization (VQ)-based generative models into the image
compression domain. The main insight is that the codebook learned by the VQGAN
model yields strong expressive capacity, facilitating efficient compression of
continuous information in the latent space while maintaining reconstruction
quality. Specifically, an image can be represented as VQ-indices by finding the
nearest codeword, which can be encoded using lossless compression methods into
bitstreams. We then propose clustering a pre-trained large-scale codebook into
smaller codebooks using the K-means algorithm. This enables images to be
represented as diverse ranges of VQ-indices maps, resulting in variable
bitrates and different levels of reconstruction quality. Extensive qualitative
and quantitative experiments on various datasets demonstrate that the proposed
framework outperforms the state-of-the-art codecs in terms of perceptual
quality-oriented metrics and human perception under extremely low bitrates.

本文提出了一种基于向量量化 (VQ) 的生成模型的图像压缩编码框架，通过学习 VQGAN 模型的码本可实现在潜空间内对连续信息进行高效压缩，从而在极低比特率下实现优质的重构图像。