Efficient Lossy Image Compression with Foundation Diffusion Models

Core Concepts

Our novel image compression codec leverages foundation latent diffusion models to synthesize lost details and produce highly realistic reconstructions at low bitrates.

Abstract

The authors propose a novel lossy image compression codec that uses foundation latent diffusion models as a means to synthesize lost details, particularly at low bitrates. The key components of their approach are: An autoencoder from a foundation latent diffusion model (Stable Diffusion) to transform an input image to a lower-dimensional latent space. A learned adaptive quantization and entropy encoder, enabling inference-time control over bitrate within a single model. A learned method to predict the ideal denoising timestep, which allows for balancing between transmission cost and reconstruction quality. A diffusion decoding process to synthesize information lost during quantization. Unlike previous work, their formulation requires only a fraction of iterative diffusion steps and can be trained on a dataset of fewer than 100k images. The authors also directly optimize a distortion objective between input and reconstructed images, enforcing coherency to the input image while maintaining highly realistic reconstructions due to the diffusion backbone. The authors extensively evaluate their method against state-of-the-art generative compression methods on several datasets. Their experiments verify that their approach achieves state-of-the-art visual quality as measured in FID, and their reconstructions are subjectively preferred by end users, even when other methods use twice the bitrate.

Stats

The authors report the following key metrics: Average encoding/decoding time of 3.49 seconds per image on an NVIDIA RTX 3090 GPU, nearly twice as fast as the CDC baseline. Model size of 1.3B parameters, with the majority coming from the Stable Diffusion backbone.

Quotes

"Our novel image compression codec leverages foundation latent diffusion models to synthesize lost details and produce highly realistic reconstructions at low bitrates." "Unlike previous work, our formulation requires only a fraction of iterative diffusion steps and can be trained on a dataset of fewer than 100k images."

Key Insights Distilled From

Lossy Image Compression with Foundation Diffusion Models

by Lucas Relic,... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08580.pdf

Deeper Inquiries

How could the authors further improve the efficiency and computational complexity of their diffusion-based compression model, potentially by incorporating more recent advances in diffusion models?

The authors could enhance the efficiency and computational complexity of their diffusion-based compression model by incorporating recent advances in diffusion models, such as leveraging more efficient backbone models. For instance, they could explore techniques like Simple Diffusion or Scalable Adaptive Computation for Iterative Generation to reduce the computational burden of the diffusion process. These newer models focus on improving the training and inference efficiency of diffusion models, which could benefit the authors' compression model by reducing training time and computational resources required. Additionally, the authors could investigate methods to optimize the diffusion process further, such as exploring different diffusion sampling strategies or incorporating techniques like continuous diffusion models. By adopting these advancements, they could streamline the compression process, making it more computationally efficient while maintaining high-quality reconstructions.

How could the authors extend their approach to support user control over the rate-distortion-realism trade-off, allowing for more flexible and customizable compression performance?

To enable user control over the rate-distortion-realism trade-off in their compression model, the authors could implement interactive features that allow users to adjust parameters influencing the compression process. One approach could be to develop a user interface where individuals can manipulate sliders or input values to fine-tune the trade-off between compression rate, distortion, and realism. Moreover, the authors could integrate a feedback mechanism where users can provide preferences or feedback on the reconstructed images. This feedback loop could be used to adapt the compression parameters dynamically based on user preferences, optimizing the trade-off according to individual user needs. Furthermore, the authors could explore reinforcement learning techniques to learn user preferences over time and automatically adjust compression parameters to align with user preferences. By incorporating user control mechanisms, the authors can offer a more personalized and customizable compression experience, catering to a diverse range of user preferences and requirements.

What are the potential ethical concerns around the use of generative models in image compression, particularly at very low bitrates where content may vary significantly from the original?

The use of generative models in image compression, especially at very low bitrates where content may deviate significantly from the original, raises several ethical concerns. One major concern is the potential for misgeneration of content, where the reconstructed images may not accurately represent the original content, leading to misinformation or misinterpretation of visual data. Another ethical consideration is the risk of bias in generative models, which could result in the generation of inappropriate or offensive content. At low bitrates, where details are scarce, generative models may struggle to maintain fidelity to the original content, potentially leading to the creation of misleading or harmful images. Moreover, there are privacy implications associated with generative models in image compression, as sensitive information in images could be distorted or altered during the compression process, compromising data integrity and confidentiality. Additionally, the use of generative models at very low bitrates may raise concerns about the authenticity and trustworthiness of compressed images, especially in critical applications such as medical imaging or forensic analysis, where accurate representation of visual data is crucial. Overall, ethical considerations around the use of generative models in image compression at low bitrates underscore the importance of transparency, accountability, and responsible deployment to mitigate potential risks and ensure the ethical use of these technologies.