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.

翻译：生成式压缩方法的最新进展在提升压缩数据感知质量方面取得了显著成效，尤其是在低比特率场景下。然而，在实现极端压缩比（<0.1 bpp）时，这些方法的有效性和适用性仍受到限制。本文通过将基于向量量化（VQ）的生成模型引入图像压缩领域，提出了一种简捷高效的编码框架。核心思想在于：VQGAN模型学习的码本具有强大的表达容量，能够在保持重建质量的同时，有效压缩潜空间中的连续信息。具体而言，通过寻找最近码字将图像表示为VQ索引，并采用无损压缩方法将其编码为比特流。进一步地，我们提出利用K均值算法将预训练的大规模码本聚类为更小的码本，从而使图像能够表示为多样化的VQ索引图范围，进而实现可变比特率与不同等级的重建质量。在多个数据集上的大量定性与定量实验表明，在极低比特率条件下，所提框架在感知质量指标与人类感知方面均优于最先进的编解码器。