{"id":745,"date":"2013-01-15T12:52:27","date_gmt":"2013-01-15T17:52:27","guid":{"rendered":"https:\/\/blogs.mathworks.com\/steve\/?p=745"},"modified":"2019-11-01T09:09:15","modified_gmt":"2019-11-01T13:09:15","slug":"data-types","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/steve\/2013\/01\/15\/data-types\/","title":{"rendered":"Digital Image Processing Using MATLAB: Data Types"},"content":{"rendered":"

Today's post is part of an ongoing tutorial series on digital image processing using MATLAB<\/a>. I'm covering topics in roughly the order used in the book Digital Image Processing Using MATLAB<\/a>.<\/i><\/p>

When working with images in MATLAB, it is important to understand how different numeric data types can come into play.<\/p>

The most common numeric data type in MATLAB is double<\/tt>, which stands for double-precision floating point<\/i>. It's the representation of numbers that you get by default when you type numbers into MATLAB.<\/p>

a = [0.1 0.125 1.3]\r\n<\/pre>
\r\na =\r\n\r\n    0.1000    0.1250    1.3000\r\n\r\n<\/pre>
class(a)\r\n<\/pre>
\r\nans =\r\n\r\ndouble\r\n\r\n<\/pre>
Double-precision floating-point numbers are intended to approximate the set of real numbers. To a reasonable degree, one can do arithmetic computations on these numbers using MATLAB (and the computational hardware on your CPU or GPU) and get the same results as \"true arithmetic\" (or \"God's math,\" as I've heard Cleve say) on the real numbers.<\/p>
Working with floating-point numbers is very useful for mathematical image processing algorithms (such as filtering, Fourier transforms, deblurring, color computations, and many others).<\/p>
Prior to 1997, the double<\/tt> was the only kind of data type in MATLAB. Image processing customers complained about this because of the memory required for these kinds of numbers. A double-precision floating-point number requires 64 bits, whereas many people working with image data were used to using only 8 bits (or even just 1 bit in the case of binary images) to store each pixel value.<\/p>
So with MATLAB 5 and Image Processing Toolbox 2 in 1997, we introduced support for a new data type, uint8<\/tt>, which is an abbreviation for unsigned 8-bit integer<\/i>. This data requires just 8 bits to represent a number, but the representable set of numbers is limited to the integers from 0 to 255.<\/p>
You can make one in MATLAB by calling the uint8<\/tt> function.<\/p>
b = uint8(5)\r\n<\/pre>
\r\nb =\r\n\r\n    5\r\n\r\n<\/pre>
class(b)\r\n<\/pre>
\r\nans =\r\n\r\nuint8\r\n\r\n<\/pre>
Also, you often see uint8<\/tt> numbers when you call the imread<\/tt> to read an image from a file. That's because image file formats often use 8 bits (prior to compression) to store each pixel value.<\/p>
rgb = imread('peppers.png'<\/span>);\r\nrgb(1:3,1:4,1)\r\n<\/pre>
\r\nans =\r\n\r\n   62   63   63   65\r\n   63   61   59   64\r\n   65   63   63   66\r\n\r\n<\/pre>
class(rgb)\r\n<\/pre>
\r\nans =\r\n\r\nuint8\r\n\r\n<\/pre>
Almost immediately after MATLAB 5 and Image Processing Toolbox 2, we started hearing from customers who had scientific data stored using 16 bits for value, so 8 bits wasn't enough and 64 bits (for double<\/tt>) still seemed wasteful. So Image Processing Toolbox 2.2 in 1999 added support for uint16<\/tt> numbers (unsigned 16-bit integers).<\/p>
But still that wasn't enough. The medical imaging community, it seemed, needed signed<\/b> 16-bit numbers. And, said many, what about single-precision floating-point?<\/p>
For Image Processing Toolbox 3 in 2001, we stopped adding data type support piecemeal and instead added support for all the data types in MATLAB at the time. Here is a summary of the entire set:<\/p>