Cross-Browser Differences Detection Based on an Empirical Metric for Web Page Visual Similarity

摘要

This article aims to develop a method to detect visual differences introduced into web pages when they are rendered in different browsers. To achieve this goal, we propose an empirical visual similarity metric by mimicking human mechanisms of perception. The Gestalt law's of grouping are translated into a computer compatible rule set. A block tree is then parsed by the rules for similarity calculation. During the translation of the Gestalt laws, experiments are performed to obtain metrics for proximity, color similarity, and image similarity After a validation experiment, the empirical metric is employed to detect cross-browser differences. Experiments and case studies on the world's most popular web pages provide positive results for this methodology.