Güzel Ahmet H, Beyazian Jeanne, Chakravarthula Praneeth, Akșit Kaan
University of Leeds, School of Computing, Leeds, UK.
University College London, Computer Science Department, London, UK.
Biomed Opt Express. 2023 Apr 21;14(5):2166-2180. doi: 10.1364/BOE.485776. eCollection 2023 May 1.
A large portion of today's world population suffers from vision impairments and wears prescription eyeglasses. However, prescription glasses cause additional bulk and discomfort when used with virtual reality (VR) headsets, negatively impacting the viewer's visual experience. In this work, we remedy the usage of prescription eyeglasses with screens by shifting the optical complexity into the software. Our proposal is a prescription-aware rendering approach for providing sharper and more immersive imagery for screens, including VR headsets. To this end, we develop a differentiable display and visual perception model encapsulating the human visual system's display-specific parameters, color, visual acuity, and user-specific refractive errors. Using this differentiable visual perception model, we optimize the rendered imagery in the display using gradient-descent solvers. This way, we provide prescription glasses-free sharper images for a person with vision impairments. We evaluate our approach and show significant quality and contrast improvements for users with vision impairments.
当今世界很大一部分人口患有视力障碍并佩戴处方眼镜。然而，在与虚拟现实（VR）头戴式设备一起使用时，处方眼镜会带来额外的厚重感和不适感，对观看者的视觉体验产生负面影响。在这项工作中，我们通过将光学复杂性转移到软件中来解决处方眼镜与屏幕的使用问题。我们的提议是一种针对屏幕（包括VR头戴式设备）的可感知处方渲染方法，用于提供更清晰、更具沉浸感的图像。为此，我们开发了一个可微的显示和视觉感知模型，该模型封装了人类视觉系统的特定于显示的参数、颜色、视力和用户特定的屈光不正。使用这个可微的视觉感知模型，我们使用梯度下降求解器在显示器中优化渲染图像。通过这种方式，我们为视力障碍者提供无处方眼镜的更清晰图像。我们评估了我们的方法，并展示了视力障碍用户在质量和对比度方面的显著改善。
