Formation of an effective pigment model from present models for better gamma encrypting in image control

Human vision is an important factor in the areas of image processing. Research has been done for years to make automatic image
handling but still human intervention cannot be denied and thus better human intervention is necessary. Two most important points are
required to improve human vision which is light and pigment. Gamma encoder is the one which helps to improve the properties of human
vision and thus to maintain visual quality gamma encrypting is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) pigment space is vastly used. Moreover, for computer
graphics RGB pigment space is called the most established choice to acquire desired pigment. RGB pigment space has a great effort on
simplifying the design and architecture of a system. However, RGB struggles to deal effectively for the images those belong to the realworld.
Images are captured using cameras, videos and other devices using different magnifications. In most cases during processing, in compare to
the original outlook the images appear either dark or bright in contrast. Human vision affects and thus poor quality image analysis may
occur. Consequently this poor manual image analysis may have huge difference from the computational image analysis outcome. Question
may arise here why we will use gamma encrypting when histogram equalization or histogram normalization can enhance images. Enhancing
images does not improve human visualization quality all the time because sometimes it brightens the image quality when it is needed to
darken and vice-versa. Human vision reflects under universal illumination environment (not pitch black or blindingly bright) thus follows an
approximate gamma or power function. Hence, this is not a good idea to brighten images all the time when better human visualization can
be obtained while darkening the images. Better human visualization is important for manual image handling which leads to compare the
outcome with the semi-automated or automated one. Considering the importance of gamma encrypting in image handling we propose an
effective pigment model which will help to improve visual quality for manual handling as well as will lead analyzers to analyze images
automatically for comparison and testing purpose.