When a physical process converts image signal to a physical image is called image processing.
To process images it is important to consider human vision. There are two important points to be
considered to improve human vision which includes light and color. Gamma encoding helps to reimburse
the properties of human vision. This is why images needs to be gamma encoded to maintain visual quality.
There is a misconception that gamma encoding has been established to reimburse for the I/O
characteristics of cathode ray tube (CRT) displays. However, gamma encoding is needed to maximize the
visual quality of the signal, apart from the gamma characteristics of the display devices. Hence, gamma
characteristics of the display device do not play a factor in the gamma encoding of images and videos.
Most images including medical images are RGB images. When images are captured using cameras using
different magnifications, the images appear either dark or bright in contrast with original outlook.
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 encoding 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 processing which leads to compare the outcome with the semi-automated
or automated one. Considering the importance of gamma encoding in image processing we propose a new
method of image analysis approach which will improve visual quality for manual processing as well as
will lead analyzers to analyze images automatically for comparison and testing purpose.
