An Application of Measuring Aesthetics in Visualization

It is challenging to efficiently and effectively manage and mine huge-sized scientific and engineering data. The challenge takes another dimension when it comes to visualization and data analytics. Without the use of detail-in-context lenses; it has been found that many important details are missed or misinterpreted. Effectiveness of visual comprehension is one of the main goals of collaborative visualization. Various detail-in-context lenses were developed to increase the visual comprehension of 2D and 3D data types by exaggerating the focused area while maintaining context such as fish-eye lenses. These lenses will enhance the cooperative collaborative visualization environment. In this work, an assessment of the effectiveness of a detail-in-context lens is presented. That is by using a set of aesthetics metrics designed to evaluate the visualizations generated by the fish-eye method to optimize the lens parameters. We have developed a framework to test these metrics on hydrocarbon reservoir simulation grids of different model sizes.