Extreme Image Compression with Latent Feature Guidance and Diffusion Prior

Core Concepts

A novel extreme image compression framework that combines compressive VAEs and pre-trained text-to-image diffusion models to achieve realistic and high-fidelity reconstructions at extremely low bitrates.

Abstract

The paper proposes a novel extreme image compression framework, named DiffEIC, that combines compressive VAEs and pre-trained text-to-image diffusion models. The key components are:

Latent Feature-Guided Compression Module (LFGCM):

Compresses images and initially decodes them into content variables using compressive VAEs.
Introduces external guidance from the latent representations in the diffusion space to dynamically modulate intermediate feature maps, improving reconstruction fidelity.

Conditional Diffusion Decoding Module (CDDM):

Leverages the powerful generative capability of pre-trained text-to-image diffusion models (e.g., Stable Diffusion) to further decode the content variables.
Injects the content information into the diffusion process through a trainable control module.

Space Alignment Loss:

Provides robust constraints for the LFGCM to ensure the content variables are well-aligned with the diffusion space.

The proposed DiffEIC framework outperforms state-of-the-art extreme image compression methods in terms of both visual performance and image fidelity at extremely low bitrates (below 0.1 bpp).

Stats

"Compressing images at extremely low bitrates (below 0.1 bits per pixel (bpp)) is a significant challenge due to substantial information loss."
"Existing extreme image compression methods generally suffer from heavy compression artifacts or low-fidelity reconstructions."

Quotes

"To address this problem, we propose a novel extreme image compression framework that combines compressive VAEs and pre-trained text-to-image diffusion models in an end-to-end manner."
"Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual performance and image fidelity at extremely low bitrates."

Key Insights Distilled From

Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior

by Zhiyuan Li,Y... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18820.pdf

Deeper Inquiries

How can the proposed framework be extended to leverage text information from pre-trained text-to-image diffusion models to further enhance the compression performance

To leverage text information from pre-trained text-to-image diffusion models and further enhance the compression performance in the proposed framework, we can introduce a text embedding module. This module can take textual descriptions as input and convert them into embeddings that can be concatenated with the latent feature guidance in the compression module. By incorporating text information, the model can better understand the semantic content of the images and use this information to guide the compression process. Additionally, the text embeddings can be used to modulate the intermediate feature maps in a similar manner to the external guidance, enhancing the alignment between the content variables and the diffusion space. This integration of text information can improve the reconstruction fidelity and perceptual quality of the compressed images, especially in scenarios where textual descriptions are available.

What advanced sampling methods could be explored to reduce the computational burden of the diffusion-based decoder in the DiffEIC framework

To reduce the computational burden of the diffusion-based decoder in the DiffEIC framework, several advanced sampling methods can be explored. One approach is to implement more efficient sampling algorithms, such as importance sampling or Markov Chain Monte Carlo (MCMC) methods, to generate samples from the diffusion model. These methods can help in generating high-quality samples with fewer computational resources. Additionally, techniques like parallel processing and distributed computing can be utilized to speed up the inference process. By leveraging the computational power of GPUs or TPUs and optimizing the implementation of the diffusion model, the decoding process can be accelerated, making it more efficient for practical applications.

How can the proposed techniques be applied to other image-related tasks, such as image restoration or generation, to leverage the benefits of combining compressive VAEs and diffusion models

The proposed techniques of combining compressive VAEs and diffusion models can be applied to various other image-related tasks to leverage their benefits in image restoration or generation. For image restoration, the framework can be adapted to perform tasks such as denoising, super-resolution, or inpainting by modifying the loss functions and training objectives accordingly. By incorporating the principles of compressive VAEs and leveraging the generative capabilities of diffusion models, the model can effectively restore images with high fidelity and perceptual quality. Similarly, for image generation tasks, the framework can be used to generate new images based on learned latent representations and external conditions. By conditioning the generation process on specific attributes or features, the model can produce diverse and realistic images that align with the desired characteristics. Overall, the proposed techniques have the potential to enhance a wide range of image-related tasks by combining the strengths of compressive VAEs and diffusion models.