Neural Image Compression with Text-guided Encoding for both Pixel-level
  and Perceptual Fidelity
Hagyeong Lee Minkyu Kim Jun-Hyuk Kim Seungeon Kim Dokwan Oh Jaeho Lee
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.