“…At low contrast levels matches seem to be in¯uenced by the detection thresholds of gratings of various areas so that matching curves resemble the contrast sensitivity (Takahashi and Ejima, 1984) or threshold (Experiment II) curves at low contrasts, but at high contrast levels matches are independent of area. Takahashi and Ejima (1984) used a test grating of a constant size and standard gratings of various sizes, meanwhile in Experiment II there were test gratings of various sizes and a standard grating of a constant size. Consequently, they found that less contrast was needed in the test grating of a constant size for a match when the standard grating was small than when it was large.…”
The purpose of the model presented in this paper is to explain the well known fact that perceived contrast becomes independent of optical low-pass and neural high-pass filtering as well as areal integration with increasing stimulus contrast. In the model we assume that perceived contrast is computed by two different parallel mechanisms. One of them integrates signal information across space to improve the signal-to-noise ratio and is affected by the optical low-pass and neural high-pass filtering. The other mechanism estimates external local contrast by using inverse filtering. These two factors are combined by a 'restoration' mechanism so that the first mechanism affects the perception of low contrast and the latter that of high contrast stimuli. The behaviour of the model was tested against experimental results obtained with normal human observers. At low contrast levels, contrast matching curves were similar in shape to the detection threshold curves both as a function of the spatial frequency and area of the grating. At high contrast levels, contrast matches became physically correct. The model described the experimental results accurately.
“…At low contrast levels matches seem to be in¯uenced by the detection thresholds of gratings of various areas so that matching curves resemble the contrast sensitivity (Takahashi and Ejima, 1984) or threshold (Experiment II) curves at low contrasts, but at high contrast levels matches are independent of area. Takahashi and Ejima (1984) used a test grating of a constant size and standard gratings of various sizes, meanwhile in Experiment II there were test gratings of various sizes and a standard grating of a constant size. Consequently, they found that less contrast was needed in the test grating of a constant size for a match when the standard grating was small than when it was large.…”
The purpose of the model presented in this paper is to explain the well known fact that perceived contrast becomes independent of optical low-pass and neural high-pass filtering as well as areal integration with increasing stimulus contrast. In the model we assume that perceived contrast is computed by two different parallel mechanisms. One of them integrates signal information across space to improve the signal-to-noise ratio and is affected by the optical low-pass and neural high-pass filtering. The other mechanism estimates external local contrast by using inverse filtering. These two factors are combined by a 'restoration' mechanism so that the first mechanism affects the perception of low contrast and the latter that of high contrast stimuli. The behaviour of the model was tested against experimental results obtained with normal human observers. At low contrast levels, contrast matching curves were similar in shape to the detection threshold curves both as a function of the spatial frequency and area of the grating. At high contrast levels, contrast matches became physically correct. The model described the experimental results accurately.
“…This function depends, amongst others, on display luminance and display size. For a standard observer it can be described by the following approximation formula: 1/N(u) = au exp(-bu) \1 i + c exp(bu) (21) 540(…”
Section: Effect Of Noise On the Threshold Modulation Depthmentioning
confidence: 99%
“…(21) for the threshold modulation depth M and by using Eq. (21) to calculate the value of M0. However, it is questionable if such a simple change would really describe the suprathreshold behavior of the eye with respect to perceived image quality.…”
Section: Application Of the Square-root Integral In The Case Of Noisementioning
confidence: 99%
“…(24) for MTF values above threshold by (25) where Mt = {l'1t02 + (kI'1)2}°' (26) and Ms = {M2 + (kN)2}°5 (27) In these equations, M0 is the modulation threshold without noise, as given by Eq. (21).…”
Section: Application Of the Square-root Integral In The Case Of Noisementioning
Basic considerations are given about the effect of static and dynamic noise on the contrast sensitivity of the eye and a numerical evaluation is made of published data on this subject. The results of this analysis are used to extend the square-root integral method for the evaluation of subjective image quality in such a way that it also can be applied to describe the effect of noise. Using an adapted version of the square-rootintegral, calculations are made for experiments with pictures with various amounts of white noise found in literature. From a comparison between measurements and calculations, it appears that the image quality calculated in this way shows a very good correlation with the measurents.
“…Another significant difference is that detection thresholds are reduced as the spatial extent of a grating is enlarged while the suprathreshold perceived contrast of a grating is unaffected by its extent (Takahashi and Ejima, 1984).…”
Section: Suprathreshold Contrast Perception In the Peripherymentioning
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.