Aliasing Detection in Rendered Images via a Multi-Task Learning

As technology advances from simple 2D designs to intricate 3D environments, the demand for high-quality visuals in video games and interactive media necessitates robust image quality assessment (IQA) techniques. Traditional methods like PSNR and SSIM, reliant on reference images, struggle with the unique challenges of 3D rendered content, highlighting the need for specialized non-reference IQA approaches. This paper introduces a novel multi-task learning architecture that corrects and predicts aliasing artifacts simultaneously, enhancing predictive accuracy without reference images. It also incorporates temporal information to improve visual coherence and smoothness. An automated labeling pipeline developed using Unity ensures a stable and unbiased dataset for model training and evaluation. Our experiments demonstrate that this approach reliably detects aliasing across various complexities, achieving state-of-the-art performance. By addressing specific challenges in rendered image assessment and leveraging innovative learning techniques, our work advances IQA for video games and simulations, ensuring high visual quality.