Abstract
Age-related macular degeneration (AMD) is the leading cause of vision loss and blindness among Americans over the age of 65. As there is currently no effective medical treatment that can reverse the central vision loss associated with AMD, digital image-processing methods have been developed to improve image visibility in the periphery, but both the selection and efficacy of such methods are currently very limited. Progress has been limited for two reasons: the types of image enhancement that benefit peripheral vision are not well understood, and efficient methods for testing such benefits have been elusive. The goal of the current study has been to simultaneously develop both an effective new image-enhancement technique for peripheral vision, and an efficient means for validating the technique. We used a novel image statistics based contour detection algorithm to locate shape-defining edges in a scene, and then enhanced the scene by locally boosting image contrast along such edges. Using a gaze-contingent display, we tested normally sighted individuals with simulated central vision loss. Visual-search performance was measured as a function of contour enhancement strength (unprocessed, "medium", and "high" enhancement). A separate group of subjects subjectively judged which image in a pair "would lead to better search performance". We found that while contour enhancement had no significant effect on response time and accuracy in younger adults (ages 18-30), the medium contour enhancement led to significantly faster search times in older adults (ages 58-88). Both age groups subjectively preferred images with medium enhancement over the unprocessed originals or the high enhancement condition. Furthermore, across age groups and enhancement levels, we found a robust relationship between preference and performance, suggesting the task-specific preference can be an efficient surrogate for performance testing. For older adults, our findings also demonstrate a beneficial role of contour enhancement for peripheral vision.
Meeting abstract presented at VSS 2012