From Multimodal to Unimodal Webpages for Developing Countries

TL;DR

This paper introduces a CCA-based method to replace high-memory web elements like images with text in developing countries, significantly reducing webpage memory costs and improving load times.

Contribution

The paper presents a novel CCA-based approach for replacing images with text on webpages, optimizing memory usage for developing countries.

Findings

Reduces memory cost by at least 83.35%

Effective in maintaining webpage functionality with lower memory

Validated through eye-tracking experiments on real webpages

Abstract

The multimodal web elements such as text and images are associated with inherent memory costs to store and transfer over the Internet. With the limited network connectivity in developing countries, webpage rendering gets delayed in the presence of high-memory demanding elements such as images (relative to text). To overcome this limitation, we propose a Canonical Correlation Analysis (CCA) based computational approach to replace high-cost modality with an equivalent low-cost modality. Our model learns a common subspace for low-cost and high-cost modalities that maximizes the correlation between their visual features. The obtained common subspace is used for determining the low-cost (text) element of a given high-cost (image) element for the replacement. We analyze the cost-saving performance of the proposed approach through an eye-tracking experiment conducted on real-world webpages. Our approach reduces the memory-cost by at least 83.35% by replacing images with text.