用AI把网页图片变小再还原，省流量又不伤画质。
PixLift: Accelerating Web Browsing via AI Upscaling
- 传输时压缩图片，手机端用AI智能放大还原。
- 实测可降低71.4万网页的流量消耗，画质几乎不变。
- 适合低带宽地区用户，也适配浏览器插件部署。
在数据资费高昂、网络受限的地区，访问互联网面临巨大挑战，限制了信息获取与经济发展。图像作为网页体积的主要来源，即便采用WebP、AVIF等先进压缩格式，仍占比较大。随着网页内容日益复杂，加之部分地区优化不足，网页大小难以有效降低。本文提出PixLift，通过在传输时下采样网页图片，并利用用户设备上的AI模型进行上采样还原，实现以计算资源换带宽。我们解决了主流网站缩放请求的可行性、浏览器插件实现及用户体验影响等问题。基于对71.4万个网页的分析、三种主流上采样模型的评估以及用户研究，结果表明PixLift能显著减少数据使用量，同时保持图像质量，推动更公平的网络接入。
原文摘要 · Abstract (English)
Accessing the internet in regions with expensive data plans and limited connectivity poses significant challenges, restricting information access and economic growth. Images, as a major contributor to webpage sizes, exacerbate this issue, despite advances in compression formats like WebP and AVIF. The continued growth of complex and curated web content, coupled with suboptimal optimization practices in many regions, has prevented meaningful reductions in web page sizes. This paper introduces PixLift, a novel solution to reduce webpage sizes by downscaling their images during transmission and leveraging AI models on user devices to upscale them. By trading computational resources for bandwidth, PixLift enables more affordable and inclusive web access. We address key challenges, including the feasibility of scaled image requests on popular websites, the implementation of PixLift as a browser extension, and its impact on user experience. Through the analysis of 71.4k webpages, evaluations of three mainstream upscaling models, and a user study, we demonstrate PixLift's ability to significantly reduce data usage without compromising image quality, fostering a more equitable internet.
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