This repository contains the Matlab implementation of the sigma predictor from:
Tursun, O. T., Arabadzhiyska-Koleva, E., Wernikowski, M., Mantiuk, R., Seidel, H. P., Myszkowski, K., & Didyk, P. (2019). Luminance-contrast-aware foveated rendering. ACM Transactions on Graphics (TOG), 38(4), 98.
In order to run the predictor please download matlabPyrTools by Simoncelli from
https://github.com/LabForComputationalVision/matlabPyrTools
and place it in the folder: ./matlabpyrtools
Then compile the MEX-files by running the following script:
./matlabpyrtools/MEX/compilePyrTools.m
and place the created files (*.mexw64, *.mexa64 or *.mexmaci64 depending on the platform) in Matlab path (see path, addpath, genpath and pathtool functions of Matlab).
main.m : Provides the implementation of sigma predictor for an image patch.
Feel free to use "help main" for info.
run_on_image.m: Runs the predictor on patches of an image and returns the
predicted sigma map.
sample_run.m: Loads a sample image from the paper and runs the predictor.
Try running this first.
get_params.m: Loads optimum predictor parameters learned using Simulated
Annealing.
display_params.m: Returns the parameters such as resolution, physical size
and the observation distance for the particular display used in our
experiments. If the physical parameters of your display are significantly
different from ours, you will need to define your display in this file and
modify run_on_image.m:11-31 where those parameters are used.
disp1_luminance.mat and disp2_luminance.mat: Provides the calibration
data for converting RGB to luminance for the displays that we used in our
experiments. The predictor expects luminance (cd/m^2) as the input. If
the calibration data does not exist, inverse gamma transformation may be
used to approximately compute linear values from sRGB (gamma = 2.2).
They are available for download at:
https://www.pdf.inf.usi.ch/projects/AdaptiveFoveation/AdaptiveFoveation.zip
Our project website:
https://www.pdf.inf.usi.ch/projects/AdaptiveFoveation