Effect of Color Saturation and Hue on Image Quality

In this paper, it will be shown that the effect of color saturation and hue on image quality can be described in a very simple way. From measurements given in this paper it appears that there is a remarkable agreement between color saturation and gamma with respect to their effect on image quality. From an earlier investigation it was found that image quality increases with the square root of gamma up to an optimum value of gamma and decreases with the inverse of the square root of gamma at higher values. It now appears that the eye reacts in a similar way on an increase of colorfulness, so that both effects can be described by the same type of equations. From measurements on the effect of hue on image quality it appears that the image quality decreases linearly with the angular rotation of the color coordinates in CIELUV or CIELAB space, if the image quality is expressed in jnds. This decrease varies symmetric with the direction of the rotation. Introduction As described in a previous paper, the eye reacts in a nonlinear way on luminance variations in an image around the average luminance. This effect can be explained by the voltage response of the cones at a variation of luminance. An example of this response is given in Figure 1. This figure shows the voltage response of the cones of a turtle measured by Burkhardt for a single adaptation luminance. At a different adaptation luminance, the curve shifts to that level. As the cones of a turtle behave similarly as that of humans, these data can be used for a general description of the visual response of the eye at a variation of luminance. The measured voltage variation can be described by the following equation: ad L c L L V + = (1) where V is the voltage expressed in relative units varying from 0 to 1, L is the luminance, Lad is the adaptation luminance, and c is a constant close to 1. The exponential slope γ of this relation can be calculated as follows: ad 1 1 ) (ln ) (ln L c L dL dV V L L d V d + = = = γ (2) For L = cLad, V = 0.5 and γ = 0.5. This means that the voltage varies with the square root of the luminance around the average luminance of an observed image. This nonlinear behavior of the visual system was taken into account in the SQRI, or square-root integral, for the description of image quality. At the time of development of this method, the here given data were not yet available, but perceptual data indicated already this behavior. The SQRI is given by the following equation: ) d(ln ) ( ) ( 2 ln 1 max min t u u m u M J u