The main purpose of this study is to build a Computational model based on ModelFest dataset which is able to predict contrast sensitivity while it benefits from simplicity, efficiency and accuracy, which makes it suitable for hardware implementation, practical uses, online tests, real-time processes, an improved Standard Observer and retina prostheses. It encompasses several components, and in particular, frequency dependent aperture effect (FDAE) which is used for the first time on this dataset, which made the model more accurate and closer to reality. Shortcomings of previous models and the necessity of existence of FDAE for more accuracy led us to develop a new model based on Wavelet Transform that gives us the advantage of speed and the capability to process each frequency channels output. Considering our goal for building an efficient model, we introduce a new formula for modeling contrast sensitivity function, which generates lower RMS error and better timing performance. Eventually, this new model leads to having as yet lowest RMS error and solving the problem of long execution time of prior models and reduces them by almost a factor of twenty.
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