Learning to predict perceptual visibility of rendering deterioration in computer games.

ABSTRACT: Contemporary computer gaming affords players the agency to manually tailor rendering settings, a capability crucial for optimizing computational demands following their hardware performance. Specifically, adjustments to texture resolution, shadow map intricacies, and anti-aliasing complexities facilitate seamless animation generation, particularly on systems equipped with budget-friendly graphical units. Nonetheless, the intricacy and extensive interdependencies among these rendering parameters render selecting an optimal configuration a multifaceted challenge. Our approach involves training a proprietary convolutional neural network (CNN) to streamline this intricate process. This CNN quantitatively compares reference images rendered at peak quality to their counterparts subjected to qual

SUBMITTER: Piorkowski R 

PROVIDER: S-EPMC11561071 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature