Gamma adjustment is one of the simplest global tone reproduction operators. If an image is too bright or too dark the image can be made pleasing by applying a gamma greater than one (leading to a darker image) or less than one (leading to a brighter image) respectively. In recent theoretical work, the ‘optimal’ gamma in an information theoretic sense has been derived. The starting point of this paper is to ask the question: in adjusting gamma in images do observers make a similar choice to the information theoretic optimum?Experimentally, we investigate the user's choice of gamma parameter by conducting double staircase psychophysical experiment on a wide range of monochrome images. Two staircases beginning with bright and dark images with respect to which gamma adjustments are made. The user progressively darkens and lightens the respective images until the staircases converge (we have the same image). The pilot experiment indicates that there is a linear relationship between the maximum entropy of image and the chosen gamma from the experiment: our experiment provides prima facie evidence that image that observers adjust images to bring out information. Moreover, the combination of entropy calculation together with our regression line we effectively provide an automatic algorithm for gamma adjustment.Finally, we also discuss the relationship between our assumption to the chosen gamma, a modified non-linear masking operator and two versions of CIECAM, and found that all of the operators give the similar trends, but slightly poorer fits, for predicting the gamma parameter. Put another way our work indicates that existing formulae for gamma adjustment can also be related to the concept of entropy maximization.