Perceptual Image Compression with Cooperative Cross-Modal Side Information

• URL: http://arxiv.org/abs/2311.13847v2
• Date: Tue, 28 Nov 2023 14:49:54 GMT
• Title: Perceptual Image Compression with Cooperative Cross-Modal Side Information
• Authors: Shiyu Qin, Bin Chen, Yujun Huang, Baoyi An, Tao Dai, Shu-Tao Xia
• Abstract summary: 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.
• Score: 53.356714177243745
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• Decoder Pre-Training with only Text for Scene Text Recognition [54.93037783663204]
  Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. We introduce a novel method named Decoder Pre-training with only text for STR (DPTR) DPTR treats text embeddings produced by the CLIP text encoder as pseudo visual embeddings and uses them to pre-train the decoder.
  arXiv  Detail & Related papers  (2024-08-11T06:36:42Z)
• Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity [18.469136842357095]
  We develop a new text-guided image compression algorithm that achieves both high perceptual and pixel-wise fidelity. By doing so, we avoid decoding based on text-guided generative models. Our method can achieve high pixel-level and perceptual quality, with either human- or machine-generated captions.
  arXiv  Detail & Related papers  (2024-03-05T13:15:01Z)
• Learned Image Compression with Text Quality Enhancement [14.105456271662328]
  We propose to minimize a novel text logit loss designed to quantify the disparity in text between the original and reconstructed images. Our findings reveal significant enhancements in the quality of reconstructed text upon integration of the proposed loss function with appropriate weighting.
  arXiv  Detail & Related papers  (2024-02-13T18:20:04Z)
• ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration [51.205673783866146]
  We present ENTED, a new framework for blind face restoration that aims to restore high-quality and realistic portrait images. We utilize a texture extraction and distribution framework to transfer high-quality texture features between the degraded input and reference image. The StyleGAN-like architecture in our framework requires high-quality latent codes to generate realistic images.
  arXiv  Detail & Related papers  (2024-01-13T04:54:59Z)
• Multi-Modality Deep Network for Extreme Learned Image Compression [31.532613540054697]
  We propose a multimodal machine learning method for text-guided image compression, in which semantic information of text is used as prior information to guide image compression performance. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions.
  arXiv  Detail & Related papers  (2023-04-26T14:22:59Z)
• Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models [13.894251782142584]
  We propose a generative image compression method that demonstrates the potential of saving an image as a short text embedding. Our method outperforms other state-of-the-art deep learning methods in terms of both perceptual quality and diversity.
  arXiv  Detail & Related papers  (2022-11-14T22:54:19Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation [52.83401421019309]
  TediGAN is a framework for multi-modal image generation and manipulation with textual descriptions. StyleGAN inversion module maps real images to the latent space of a well-trained StyleGAN. visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. instance-level optimization is for identity preservation in manipulation.
  arXiv  Detail & Related papers  (2020-12-06T16:20:19Z)
• How to Exploit the Transferability of Learned Image Compression to Conventional Codecs [25.622863999901874]
  We show how learned image coding can be used as a surrogate to optimize an image for encoding. Our approach can remodel a conventional image to adjust for the MS-SSIM distortion with over 20% rate improvement without any decoding overhead.
  arXiv  Detail & Related papers  (2020-12-03T12:34:51Z)
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis [80.54273334640285]
  We propose a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators. We also propose a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images.
  arXiv  Detail & Related papers  (2020-08-13T12:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.