# Modeling Luminance Perception at Absolute Threshold

 Petr Kellnhofer1 Tobias Ritschel1,2 Karol Myszkowski1 Elmar Eisemann3 Hans-Peter Seidel1

Proceedings of Eurographics Symposium on Rendering 2015, Darmstadt, 24-26th June 2015

## Inverse CDF lookup tables

Here we provide lookup tables that we used to sample probability distributions on the GPU. We loaded provided table values as textures and used texture sampling in OpenGL shader to read them in real time.

Each inverse cumulative distribution function (ICDF) contains matrix of values separated by spaces and stored as a text file. First row contains number of rows and columns. Following lines each contain one row of a matrix. Each row corresponds to one ICDF of distribution with certain parameters (see lower). The columns uniformly sample percentiles of respective CDF and should be accessed using uniform random variable to achieve sampling of original probability distribution function (pdf). Each value is a value for given percentile of CDF.

• Poisson distribution

Inverse function for F(Θ, μ) from the paper. Used to sample Quantization noise (Eq. 1 in the paper) and Photon-like noise (Eq. 2). Each row corresponds to single value of parameter λ of Poisson distribution starting from 1 and sequentially going to 1023.

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• Combined noise distribution

Inverse function for CDF of probability distribution function PAll(k, N) from Eq. 3 in the paper. Each row corresponds to single value of parameter N starting from 1 and sequentially going to 1023.

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• Binomial distribution with p = 0.0096

Inverse function for CDF binomial distribution from PFinal(M,N') in the paper. It assumes that there are 4.8 rods per pixel, therefore p = 0.0096. This matches our 22'' 1680x1050 display with 0.254 mm pixels observed from 60 cm and it should approximate common PC setups well. Each row corresponds to single value of parameter M starting from 1 and sequentially going to 1023.

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