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What is Digital Data


What is Digital Data?

Digital Data

Images with a continuous gray tone or color, like a photograph are called analog images. On the other hand, a group of divided small cells, with integer values of average intensity, the center representing the cell's value, is called a digital image. The spatial division into a group of cells is called sampling as illustrated in Figure 1, while conversion of analog images into integer image data is called quantization as illustrated in Figure 2 and 3.
                    

Figure 1: Concept of sampling


Figure 2: Concept of quantization


 
                                 

Figure 3: Quantization in the case of a signal containing a noise



An individual divided cell is called a pixel (picture cell). The shape of the cell is usually square for easy use in a computer, though triangular or hexagonal can also be considered.
A digital image has coordinates of pixel number, normally counted from left to right, and line number, normally counted from top to bottom.
The most important factor in sampling is pixel size or sampling frequency. If the pixel size is large or the sampling frequency is long, the appearance of the image becomes worse, while in the reverse case the data volume becomes very large. Therefore the optimum sampling should be carefully considered.
Shannon's sampling theorem, for specifying the optimum sampling, is given as follows.
"There will be no loss of information if sampling is taken with a half frequency of the maximum frequency involved in the original analog frequency wave."
Let the analog intensity be f and the unit intensity v (>0) as divider in quantization. Let the quantized intensity be fd, fd is given by n as illustrated Figure 2. The difference between f and fis called quantization error.
The question is how to determine the number of quantization levels or the unit intensity as divider. If the number of levels is too small, the quantization error will increase. In the reverse, the data volume increases with information less data because of the noise level, as shown in Figure 3.
For example in Figure 3, the quantization should be divided by a level larger than that of the noise. In this example, four levels would be an appropriate quantization.
 

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