PixLift: Accelerating Web Browsing via AI Upscaling
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
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.
