It is known that there are already individual differences in the de-tection phase of the human color vision system, for example in the spatial configuration or spectral sensitivities of the cone cells in the eye [34, 66]. The increasing amount of knowledge about human color vision properties has raised a need to re-evaluate the existing color vision model. We have started this work with the following publications. The relationship of each publication to certain aspects of color vision is shown in Figure 5.1.
[P3] Spectral images and the Retinex model [P2] Cone ratio
in color vision models
[P5] The effect of stimulus color, size and duration
in color naming reaction times [P1] Comparison of
color vision models based on spectral color representation
[P4] Color classification using color vision models General properties of the
human color vision system
Color constancy
Color naming Color classification Color perception Color naming
?
Figure 5.1: Publications
In publication [P1](Comparison of Color Vision Models Based on Spectral Color Representation), we re-evaluate four existing color vision models and consider questions raised by recent break-throughs in retinal imaging. Many color vision models are based on an
as-sumption of the standard observer, and usually there is no con-sideration regarding how the individual properties of different ob-servers could be taken into account. For example, the retinal mosaic or the cone sensitivities can vary a lot between observers, which can be taken into account by starting the color vision process from the spectral origin.
The main interest in this article was to examine how different color vision models behave when compared to corresponding hu-man results. The evaluation presented in this paper gives us more knowledge about the essential properties of the models. The perfor-mance of the models in the Farnsworth-Munsell 100 Hue color vi-sion test, the wavelength discrimination power of each model, and the color spaces spanned by the models were examined. Guth’s complex nonlinear model was found to be able to approximate the color vision properties over a wide range of luminances, but also the lot simpler Ingling and Tsou’s linear model with a single oppo-nent stage was able to give adequate results. However, no model was able to replicate the performance of human color vision fully in every experiment, and our experiments show that there are large differences in the properties of these models.
In publication[P2](Cone Ratio in Color Vision Models), we consid-ered how the changes of the cone ratio in the retina would affect color vision models. The basis of our analysis lies in a Multi-Stage Color Model by de Valois and de Valois (1993), and we show how changes in cone ratio affect the different stages of this model. The behavior of the Multi-Stage Color Model was tested with different cone ratios: 10:5:1, 18:5:1, 3:6:1, 12:1:1 and 1:1:1. The changes in cone ratio were implemented as changes in the weighting of the re-sponses at the first stage of the model. For the Farnsworth-Munsell 100 Hue color vision test, ratio 3:6:1 best preserved the organiza-tion of the test colors in the opponent color space. Ratios 12:1:1 and 1:1:1 differed most from the others. This article shows that if the changes in cone ratio would not be compensated for at all in the later stages of the human visual system, the resulting color space
Summary of the Results
would be very different for each individual.
In publication [P3] (Spectral images and the Retinex model), the color constancy performance of the Retinex algorithm in different color spaces was reviewed.The Retinex algorithm has earlier been applied mainly to grayscale or RGB images, but in this paper we consider different ways of applying the Retinex model to spectral images. The use of spectral images as the starting point for the model enables one, for example, more accurate examination of il-lumination or observer changes. First, we tested how the Retinex preserves colors when the spectral power distribution of the am-bient light changes. To examine this, the algorithm was applied to images in four different color spaces: spectral, L channel of the L*a*b*, LMS, and sRGB. The color constancy between different il-luminants was best preserved by using the spectral color space, and the poorest performance was reached by using the sRGB space.
When examining the behavior of the Retinex model in a case where the surround of an area of a scene changes, the results in RGB and spectral spaces are similar (contrary to the first experiment) and closer to the real world color perception than the results in other color spaces. In general, it was noted that the output of the Retinex algorithm for all color spaces requires careful selection of the post-processing method.
In publication[P4](Color classification using color vision models), we look at the color vision topic from a classification point of view.
Instead of detecting and analyzing colors exactly in the same way, we have all just learned to classify colors in a certain way, which seems to lead almost always to the same result independent of the individual differences in the color vision system. The color clas-sification abilities of color vision models, considered also in [P1], were examined by using a simple subspace classification method on Munsell Matte Collection color samples. The output of the Multi-Stage Color Model by De Valois and De Valois was a very poor starting point for color classification, and with the output of Ingling and Tsou model the color classes were somewhat overlapping. The
two nonlinear models, Bumbaca and Smith’s and Guth’s models, performed significantly better in this classification task. The errors made by the two latter models were similar to the ones human ob-servers also often make, meaning that if the color was not classified into the correct class, it was usually classified into one of the neigh-boring classes.
In publication [P5] (The effect of stimulus color, size and dura-tion in color naming reacdura-tion times), we examine the differences in color naming reaction times between subjects with normal and deficient color vision. Color deficient people use, in many cases, color names in a similar manner as people with normal color vi-sion do. This means that the individual differences at the detection level of a color vision system are somehow compensated for at the later levels. In order to examine how different parameters affect the color naming process, we conducted a color naming experiment by using modified Berlin and Kay (1969) focal colors of different sizes and durations, and measured the reaction time for each stimulus.
Some differences were found between normal and color vision deficient subjects. It was found that among the three examined parameters, color, size and duration, most differences in reaction times between color vision deficient subjects and subjects with nor-mal color vision were due to the color of the stimuli. Color vision deficient (protan and deutan) subjects were clearly slower to react to red and green colors than to blue and yellow, whereas there were no remarkable differences in the reaction times for different colors with subjects having normal color vision. There was one deutan subject who consistently named red colors as green. In addition, subjects with normal color vision named very small blue stimuli as green. Also, the size of the stimuli had an effect on the reaction times for some subjects, and size and duration both affected the misclassification rate of stimuli.
6 Conclusions
As almost always when modeling natural phenomena, when mod-eling color vision the following questions also easily arise. Should the model be universal or specific just for a certain application? At what level of accuracy should the model be implemented? Is it even possible to create a universal model?
Understanding the behavior of human color vision requires knowl-edge from various fields of science. It makes no sense to claim that the overview presented here would be all-embracing, because there are countless possibilities for implementing the properties of hu-man color vision. This dissertation and the related articles have been written with an intention to contemplate the properties of hu-man color vision from a slightly different point of view.
It was found out in[P1]that none of the examined models were able to replicate the behavior of human color vision perfectly in all experiments. At least some kind of nonlinearity had to be im-plemented in order to be able to compensate for the differences between different brightness levels. Even though the performance of the Bumbaca and Smith model was the poorest with the tasks given in [P1], the model performed well in the color classification task in[P4]. This is a good example of a model that performs well in a specific task for which it has been originally designed, in this case the performance being motivated by a computer vision appli-cation. Guth’s ATD model performed well in almost all given tasks, but from the ATD model it is not as easy to separate all the stages of the biological color vision system as it is, for example, in the De Valois and De Valois model. As models are based on different ideas, they also perform differently in given tasks.
Based on the results of [P2], we can speculate that if the only thing changing anatomically in the color vision system between in-dividuals was the cone ratio on the retina, the personal color space of each individual would be quite different from others. Of course
it is quite a radical simplification of the process if only the cone ra-tio in the model is changed, and this does not tell the whole truth of what actually happens in the human color vision. When peo-ple have different cone distribution on the retina, the further neural connections related to color vision are most probably formulated in a different way, too. The actual spatial arrangement of the cones was not considered as a part of this dissertation, but it is also one interesting research topic related to color vision modeling.
Color constancy is one important property of the human color vision system, but it is not too easy to model, as was seen in [P3].
Also the connections between different colored stimuli and the ac-tual color sensation can vary a lot depending on the parameters related to the stimuli, as was shown in the experiments in[P5]. The largest differences in color-related issues are typically seen between subjects with normal and deficient color vision, but there are also individual differences within each group.
All these results strengthen the idea that color vision as a con-cept is complicated, and that the modeling of it well is a demanding task. The adaptive behavior of the color vision system is important from the modeling point of view: in order to be able to replicate the color vision accurately, a model must also have dynamic prop-erties, like possibilities for spatial and temporal processing. It is very likely that no model can take into account every single aspect of color vision, but by combining the best properties of each model it is possible to find a more universal approach.
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