Space-Mag: An Automatic, Scalable, and Rapid Space Compactor for Optimizing Smartphone App Interfaces for Low-Vision Users

Low-vision users interact with smartphones via screen magnifiers, which uniformly magnify raw screen pixels, including whitespace and user interface (UI) elements. Screen magnifiers thus occlude important contextual information, such as visual cues, from the user's viewport. This requires low-vision users to pan over the occluded portions and mentally reconstruct the context, which is cumbersome, tiring, and mentally demanding. Prior work aimed to address these usability issues with screen magnifiers by optimizing the representation of UI elements suitable for low-vision users or by magnifying whitespace and non-whitespace content (e.g., text, graphics, borders) differently. This paper combines both techniques and presents SpaceXMag, an optimization framework that automatically reduces whitespace within a smartphone app, thereby packing more information within the current magnification viewport. A study with 11 low-vision users indicates that, with a traditional screen magnifier, the space-optimized UI is more usable and saves at least 28.13% time for overview tasks and 42.89% time for target acquisition tasks, compared to the original, unoptimized UI of the same app. Furthermore, our framework is scalable, fast, and automatable. For example, on a public dataset containing 16, 566 screenshots of different Android apps, it saves approximately 47.17% of the space (area) on average, with a mean runtime of around 1.44 seconds, without requiring any human input. All are indicative of the promise and potential of SpaceXMag for low-vision screen magnifier users. https://doi.org/10.1145/3596253