Recent advances in text-guided image compression have shown great potential
to enhance the perceptual quality of reconstructed images. These methods,
however, tend to have significantly degraded pixel-wise fidelity, limiting
their practicality. To fill this gap, we develop a new text-guided image
compression algorithm that achieves both high perceptual and pixel-wise
fidelity. In particular, we propose a compression framework that leverages text
information mainly by text-adaptive encoding and training with joint image-text
loss. By doing so, we avoid decoding based on text-guided generative models --
known for high generative diversity -- and effectively utilize the semantic
information of text at a global level. Experimental results on various datasets
show that our method can achieve high pixel-level and perceptual quality, with
either human- or machine-generated captions. In particular, our method
outperforms all baselines in terms of LPIPS, with some room for even more
improvements when we use more carefully generated captions.

近期在文本引导的图像压缩方面的研究取得了巨大潜力，能够增强重构图像的感知质量，但这些方法往往在像素级保真度方面明显降低，限制了它们的实用性。为了填补这一空白，我们开发了一种新的文本引导图像压缩算法，实现了高感知和像素级保真度，该压缩框架主要通过文本自适应编码和联合图像 - 文本损失的训练来利用文本信息。通过这样做，我们避免了基于文本引导生成模型的解码，这种模型以高度生成多样性著称，并有效地利用了文本的语义信息。各种数据集上的实验结果表明，我们的方法在像素级和感知质量方面均可达到较高水平，无论是人类生成的标题还是机器生成的标题。特别地，在与其他基准方法相比的 LPIPS 方面，我们的方法表现出色，并且在使用更加精心生成的标题时，仍有进一步提高的空间。