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：近年来，生成式压缩方法的进展显著提升了压缩数据的感知质量，尤其在低比特率场景中表现突出。然而，其在实现极端压缩比（<0.1 bpp）方面的有效性和适用性仍面临限制。本文提出一种简洁高效的编码框架，通过将基于矢量量化（VQ）的生成模型引入图像压缩领域。核心思想在于：VQGAN模型学习到的码本具有强大的表达能力，能够在保持重建质量的同时，有效压缩潜在空间中的连续信息。具体而言，图像可通过寻找最近码字表示为VQ索引，并利用无损压缩方法将其编码为比特流。进一步，我们提出利用K-means算法将预训练的大规模码本聚类为更小的码本，从而将图像表示为不同范围的VQ索引图，实现可变比特率与不同级别的重建质量。在多个数据集上的充分定性与定量实验表明，所提框架在极低比特率下，基于感知质量指标与人类主观感知均优于当前最优编解码器。