Sensitivity to variations in luminance has been extensively studied via detection thresholds to give the well-known threshold versus intensity (TvI) function. The function is expansive – the lower the luminance level, the lower the threshold. However, when a pedestal is introduced such that the task is to discriminate between the luminance of two patches superimposed upon a uniform background, the results are substantially more complex. Thresholds are both low at the lowest luminance levels tested and additionally around the background luminance level, an effect termed ‘crispening’. This has lead authors to propose two separate mechanisms, one more sensitive to low luminance levels and another to contrast around the background luminance level. In this paper, we model discrimination thresholds via a single mechanism. We assume that the maximal sensitivity of the HVS is well modeled by the shape of the TvI function, but in the case of non-uniform backgrounds this function is modulated by a gain control mechanism that increases thresholds away from the background luminance level. We evaluate our model upon the data of Paul Whittle (1986) who examined discrimination threshold over a broad luminance range and also upon new and old data for functions exhibiting various levels of ‘crispening’ (Nagy and Kamholz, 1995). Second, in keeping with the work of Paul Whittle (1992) we investigate whether this model can predict supra-threshold brightness functions. We find that as long as a realistic (a non-Weber) TvI function is used, that the brightness functions can be accurately estimated. In the case of non-uniform backgrounds (salt and pepper noise or the inclusion of an annulus), the model requires an additional gain term for each background luminance level. Although this adds to the complexity of the model, it offers the possibility of extending the model to arbitrarily complex stimuli.
Funding Sources: This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government and FEDER Fund, grant ref. TIN2015-71537-P(MINECO/FEDER,UE), and by the Icrea Academia Award.