ISO Preference Curves in Digital Image Processing
What is ISO Preference?
In most basic terms, ISO preference is the graphical representation of quantified values of image quality whose points all mention images that are of constant subjective quality. ISO preference curves are basically used to demonstrate the effects produced on image quantities. It happens by changing the values of N and K independently. Here, N is the total number of pixels in an image and K refers to the total number of bits in each pixel of that image.
What is Contouring?
Contouring is a property of images. As the number of grey levels, false colours, edges in an image decreases, it refers to the contouring of an image. When these things start getting visible on images, it is called contouring.
Increase And Decrease In Contouring
The contouring is inversely proportional to the total number of grey lines in an image. As the number of grey lines increases, contouring decreases, and vice versa. This effect also depends on the quantization of an image. Quantization is directly proportional to contouring. As the quantization increases, contouring also increases and vice versa.
Now, there is one more factor that is highly responsible for contouring which is ISO preference. It is discussed in more detail below with an example.
Let’s understand ISO preference curves in digital image processing by an example. So when ISO preference curves were discovered, the scientist used three images to demonstrate it and how it works. Those three images were something like these given below-
Some observations about the image-
- The first one is a picture of a girl, having low details. It has low details because there is only a girl’s face in the picture with just some components such as eyes, nose, lips, hair, etc.
- The second image is of a photographer with a moderate level of detail. Since the image is of a full man with a camera, therefore the total number of details is quite high in this image as compared to the first one.
- And the last one is of a crowd with so many details. Since it is a crowd with many people, therefore the picture is filled with a lot of components increasing its details.
In the above observations, details refer to various things such as sharpness, brightness, contrast, colors, etc of an image. Details are directly related to contouring. For example, as the number of details in an image increases, the contouring affects stars appearing on the image later onwards. And when images have less amount of detail, it happens due to quantization of grey levels.
Therefore, the researcher took these images only because due to highly varying details, contouring effects were varied and hence its properties can be understood properly.
Later on, when these images were observed carefully studying all of its properties, a graph was made, something like this-
This was the result after properly analyzing all the images. Each curve in the graph is the representation of one image. As we discussed above the factors K and N, are used to plot this graph. The x-axis is the representation of the total number of pixels and the y-axis represents the total number of bits in a pixel.
Therefore in simple words, these are some points as the final result of the experiment-
- The effect of contouring in the first image started easy as compared to other ones.
- The effect of contouring in the second image started to show after some time when the number of grey lines reduced to a minimum.
- And the third image was subject to contouring after a long time due to a high number of details.
Digital image processing is the process of analyzing the digital properties of an image. In today’s time, all these things are done by digital computers. There are many phases of digital imaging processing which include image enhancement, restoration, color image process, etc. therefore, all these processes contributed to digital image processing.
In most cases, it is done to extract important information from an image and remove waste information. In this way, an image is altered based upon too many factors.
As we discussed above ISO preferences, it is an important component of the digital image processing phase. All the things that we discussed ISO preferences are highly used in image processing to understand various properties of an image such as grey level, contouring, etc which plays an important role in understanding an image properly.