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.

翻译：随着社交媒体、网页浏览和视频流媒体的快速发展，图像已成为Web体验的核心要素，这导致了显著的数据传输量增长和网页体积增大。传统图像压缩方法虽能降低带宽占用，却常以牺牲图像质量为代价。本文探索了一种利用生成式人工智能在边缘或客户端重建图像的新方法。我们开发了一个框架，该框架利用文本提示并向文本到图像模型提供如Canny边缘和调色板等附加条件输入，在最佳情况下可实现高达99.8%的带宽节省，平均节省率达92.6%，同时保持较高的感知相似度。实证分析和用户研究表明，与传统压缩方法相比，我们的方法能更有效地保留图像语义与结构，为降低带宽使用、提升互联网经济性提供了一种在图像质量损失最小化的前景广阔的解决方案。