ChromaCorrect: Prescription Correction in Virtual Reality Headsets through Perceptual Guidance

Project Page | Video | Paper

Ahmet H. Güzel*¹, Jeanne Beyazian², Praneeth Chakravarthula³, Kaan Akşit*²,

¹University of Leeds, ³Princeton University, ²University College London, ^*Corresponding Authors

What is ChromaCorrect?

A large portion of today’s world population suffer from vision impairments and wear prescription eyeglasses. However, eyeglasses causes additional bulk and discomfort when used with augmented and virtual reality headsets, thereby negatively impacting the viewer’s visual experience. In this work, we remedy the usage of prescription eyeglasses in Virtual Reality (VR) headsets by shifting the optical complexity completely into software and propose a prescriptionaware rendering approach for providing sharper and immersive VR imagery. To this end, we develop a differentiable display and visual perception model encapsulating display-specific parameters, color and visual acuity of human visual system and the user-specific refractive errors. Using this differentiable visual perception model, we optimize the rendered imagery in the display using stochastic gradient-descent solvers. This way, we provide prescription glassesfree sharper images for a person with vision impairments. We evaluate our approach on various displays, including desktops and VR headsets, and show significant quality and contrast improvements for users with vision impairments.

Description

This repository contains our implementation for learning prescriptions related to refractive vision problems (myopia, hyperopia).

Clone the entire repository and navigate to the root directory.

shell git clone git@github.com:complight/ChromaCorrect.git

Install the required dependencies

requirements.txt can help you to install the required packages using pip:

shell pip3 install -r requirements.txt

Running the code (performing optimization)

Once you have the requirements successfully installed, you are ready to run the optimisation.

shell python3 main.py --device cuda --diopter -2.0 --filename dataset/parrot.png --directory sample --backlight read

You can also adjust the parameters used in the optimization routine by passing arguments. To learn more about parameters:

shell python3 main.py --help

Dataset

You can use images from Dataset for your experiments.

Images are selected from combination of DIV2K, CLL and Kodak datasets.

Importing Display Backlight Data

File names have to be named exactly as below : - redspectrum.csv - greenspectrum.csv - blue_spectrum.csv

inside .csv, format should be below (column1 : wavelength, column2 : spectrum data)

column1     column2
400         spectrum[0]
.
.
.
700         spectrum[i]

Citation

```bibtex @article{Guzel:23, author = {Ahmet H. G\"{u}zel and Jeanne Beyazian and Praneeth Chakravarthula and Kaan Akșit}, journal = {Biomed. Opt. Express}, keywords = {Holographic displays; Image metrics; Image quality; Vision modeling; Visual acuity; Visual system}, number = {5}, pages = {2166--2180}, publisher = {Optica Publishing Group}, title = {ChromaCorrect: prescription correction in virtual reality headsets through perceptual guidance}, volume = {14}, month = {May}, year = {2023}, url = {https://opg.optica.org/boe/abstract.cfm?URI=boe-14-5-2166}, doi = {10.1364/BOE.485776}, }

```