# A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing
## 4 Evaluation
### PCT in the Wild  
To investigate the effectiveness of PCT in the wild, a publicly available dataset of videos of web browsing is used. The dataset includes 83 videos for a range of websites from various content categories. For each website, PCT, Objective ATF, and SI are computed.  
The left subplot of Figure 10 illustrates the relationship between objective ATF (\(y\)-axis) and PCT (\(x\)-axis). For the majority of the test cases, the estimated ATF time using objective ATF is higher than the corresponding objective ATF computed using PCT. To quantify this, a R-squared of 0.55 is computed. The computed R-squared indicates that PCT is only explaining 55% of the variation in objective ATF. Despite the fact that both metrics are trying to estimate ATF time, PCT and objective ATF are quite different in estimating ATF time.  
To explore the impact of ATF estimation using objective ATF and PCT, computed SIs are visualized in the right subplot of Figure 10. The x-axis shows SI computed using PCT and y-axis shows SI computed using objective ATF. Except in some cases, both variations of SI yield to a similar result with a computed R-squared of 0.88.  
A post-analysis verification performed by the authors (Figure 11) shows that, for the websites with non-animated content, both PCT and ATF are detecting the same video frame as an ATF time. However, for the websites with delayed privacy or GDPR popups, layout shifting, animated content, and placeholders with background colors, PCT is detecting the ATF time sooner than the objective ATF. Several studies confirm that the web users visually and cognitively ignore pop-ups [22; 23]. Thus, it can be understood that PCT operates well on the wide variety of issues and scenarios that occur beyond a controlled laboratory setup.  
Figure 9: Using PCT to estimate ATF for use in the Waiting time and its QoE evaluation on a linear ACR scale is Logarithmic (WQL) and IQX Quality of Experience (QoE) models. The **(left)** subplot shows a high positive linear correlation between QoE estimation of WQL using PCT (y-axis) and the ground truth perceived ATF (x-axis). The **(right)** subplot represents a positive correlation between QoE estimation of IQX using PCT (y-axis) and perceived ATF time (x-axis). Comparing these results with those presented in Reference [10] shows that PCT has positively improved QoE estimation for IQX and comparable results for WQL.  
Figure 11: PCT estimation on a public data-set. In the **(left)** column of subplots, the blue lines represents the cumulative visual progress of PCT, purple bars are the Similarity Index Measure (SSIM) scores between two consecutive frames, the dotted green line is the PCT frame time, and the solid red line shows the objective ATF frame. The **(middle)** column shows the frames associated with PCT and the **(right)** column shows the objective ATF frames.  
Figure 10: The performance evaluation of PCT in the wild. The **(left)** subplot represents positive linear relationship and some degree of agreement on ATF estimation between PCT (x-axis) and the Objective ATF (y-axis). The **(right)** subplot shows a tight positive linear relationship between SI estimated based on PCT (x-axis) and SI estimated using objective ATF. Since the majority of the test cases are the website without animations, the result confirms that computing SI using PCT yield a similar result as the SI computed based on state-of-the-art ATF metric. A post-analysis verification shows that the majority of the outliers are related to the websites with animations, pop-ups, or late loading contents (see Figure 11).