Formation of an effective pigment model from present models for better gamma encrypting in image control (International Journal of Scientific Research and Management (IJSRM), 2014)
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.