Rethinking Image Compression on the Web with Generative AI

The rapid growth of the Internet, driven by social media, web browsing, and video streaming, has made images central to the Web experience, resulting in significant data transfer and increased webpage sizes. Traditional image compression methods, while reducing bandwidth, often degrade image quality. This paper explores a novel approach using generative AI to reconstruct images at the edge or client-side. We develop a framework that leverages text prompts and provides additional conditioning inputs like Canny edges and color palettes to a text-to-image model, achieving up to 99.8% bandwidth savings in the best cases and 92.6% on average, while maintaining high perceptual similarity. Empirical analysis and a user study show that our method preserves image meaning and structure more effectively than traditional compression methods, offering a promising solution for reducing bandwidth usage and improving Internet affordability with minimal degradation in image quality.

Evaluating the feasibility of using Generative Models to generate Chest X-Ray Data

In this paper, we explore the feasibility of using generative models, specifically Progressive Growing GANs (PG-GANs) and Stable Diffusion fine-tuning, to generate synthetic chest X-ray images for medical diagnosis purposes. Due to ethical concerns, obtaining sufficient medical data for machine learning is a challenge, which our approach aims to address by synthesising more data. We utilised the Chest X-ray 14 dataset for our experiments and evaluated the performance of our models through qualitative and quantitative analysis. Our results show that the generated images are visually convincing and can be used to improve the accuracy of classification models. However, further work is needed to address issues such as overfitting and the limited availability of real data for training and testing. The potential of our approach to contribute to more effective medical diagnosis through deep learning is promising, and we believe that continued advancements in image generation technology will lead to even more promising results in the future.