# Perceptual Image Compression with Cooperative Cross-Modal Side Information

arXiv: https://arxiv.org/abs/2311.13847 · HTML: https://mlsanity.com/paper/2311.13847/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance](https://mlsanity.com/paper/2204.08583/) (2022)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [The 2018 PIRM Challenge on Perceptual Image Super-resolution](https://mlsanity.com/paper/1809.07517/) (2018)
- [Text to Image Generation with Semantic-Spatial Aware GAN](https://mlsanity.com/paper/2104.00567/) (2021)
- [Deep Semantic Dictionary Learning for Multi-label Image Classification](https://mlsanity.com/paper/2012.12509/) (2020)
- [Multi-Modality Deep Network for Extreme Learned Image Compression](https://mlsanity.com/paper/2304.13583/) (2023)
- [Deep Image Compression using Decoder Side Information](https://mlsanity.com/paper/2001.04753/) (2020)
- [Cross Modal Compression: Towards Human-comprehensible Semantic Compression](https://mlsanity.com/paper/2209.02574/) (2022)
- [Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain](https://mlsanity.com/paper/2209.02514/) (2022)

## Built on by (most cited first, 2 of 2)

- [Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity](https://mlsanity.com/paper/2403.02944/) (2024)
- [Towards Efficient Low-rate Image Compression with Frequency-aware Diffusion Prior Refinement](https://mlsanity.com/paper/2601.10373/) (2026)

## Abstract (arXiv)

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.