Abstract
Several researchers in the field of lightness/brightness perception propose computational models that aim to accurately predict human perception. Although models that employ spatial filters is the major approach in this research topic, a novel computational model called Markov illuminance and reflectance (MIR) employs a probabilistic method and demonstrates a high level of performance in predicting lightness illusions. In addition, MIR is extensible; thus, it can be a novel pathway for the pursuit of better models for lightness/brightness. However, its availability is restricted, because the original code of MIR is provided in MATLAB, which is a relatively expensive language. Moreover, processing even extremely small images takes a relatively long time due to the complexity of the computation of MIR. To address these concerns, the current study translates the original code into two free languages, namely, Python and Julia. Python is one of the most popular languages; thus, Python implementation should render MIR open to more people. Alternatively, Julia is currently less popular than Python or MATLAB; however, it is known to run fast with simple coding. The study notes that the Julia implementation ran nearly three times faster than the original MATLAB code in our environment. To improve existing models and approach an ideal one, repeatedly testing models using a large number of images is indispensable, and the proposed models must be testable by anyone. Therefore, the free and fast implementations of MIR presented in the study should be a beneficial tool for constructing a better computational representation of lightness/brightness perception in humans.