PixLift: Accelerating Web Browsing via AI Upscaling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909490861834240 |
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| author | Atinafu, Yonas Malla, Sarthak Jang, HyunSeok Daniel Aldahoul, Nouar Varvello, Matteo Zaki, Yasir |
| author_facet | Atinafu, Yonas Malla, Sarthak Jang, HyunSeok Daniel Aldahoul, Nouar Varvello, Matteo Zaki, Yasir |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_08995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PixLift: Accelerating Web Browsing via AI Upscaling Atinafu, Yonas Malla, Sarthak Jang, HyunSeok Daniel Aldahoul, Nouar Varvello, Matteo Zaki, Yasir Performance Artificial Intelligence 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. |
| title | PixLift: Accelerating Web Browsing via AI Upscaling |
| topic | Performance Artificial Intelligence |
| url | https://arxiv.org/abs/2502.08995 |