  The explosion of data has resulted in more and more associated text being
transmitted along with images. Inspired by from distributed source coding, many
works utilize image side information to enhance image compression. However,
existing methods generally do not consider using text as side information to
enhance perceptual compression of images, even though the benefits of
multimodal synergy have been widely demonstrated in research. This begs the
following question: How can we effectively transfer text-level semantic
dependencies to help image compression, which is only available to the decoder?
In this work, we propose a novel deep image compression method with text-guided
side information to achieve a better rate-perception-distortion tradeoff.
Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial
Aware block to fuse the text and image features. This is done by predicting a
semantic mask to guide the learned text-adaptive affine transformation at the
pixel level. Furthermore, we design a text-conditional generative adversarial
networks to improve the perceptual quality of reconstructed images. Extensive
experiments involving four datasets and ten image quality assessment metrics
demonstrate that the proposed approach achieves superior results in terms of
rate-perception trade-off and semantic distortion.
