An image is really just a collection of pixels of differing intensities, regardless of whether it is a grayscale (achromatic) or colour image. Exploring the pixels collectively helps provide an insight into the statistical attributes of an image. One way of doing this is by means of a *histogram*, which represents statistical information in a visual format. Using a histogram it is easy to determine whether there are issues with an image, such as over-exposure. In fact histograms are so useful that most digital cameras offer some form of real-time histogram in order to prevent poorly exposed photographs. Histograms can also be used in post-processing situations to improve the aesthetic appeal of an image.

A *histogram* is simply a frequency distribution, represented in the form of a *graph*. An image histogram, sometimes called an *intensity histogram*, describes the frequency of intensity (brightness) values that occur in an image. Sometimes as in Figure 1, the histogram is represented as a bar graph, while other times it appears as a line graph. The graph typically has “brightness” on the horizontal axis, and “number of pixels” on the vertical axis. The “brightness” scale describes a series of values in a linear scale from 0, which represents black, to some value `N`

, which represents white.

A image histogram, `H`

, contains `N`

*bins*, with each bin containing a value representing the number of times an intensity value occurs in an image. So a histogram for a typical 8-bit grayscale image with 256 gray levels would have `N`

=256 bins. Each bin in the histogram, `H[i]`

represents the number of pixels in the image with intensity i. Therefore `H[0]`

is the number of pixels with intensity 0 (black), `H[1]`

the number of pixels with intensity 1, and so forth until `H[255]`

which is the number of pixels with the maximum intensity value, 255 (i.e. white).

A histogram can be used to explore the overall information in an image. It provides a visual characterization of the intensities, but does not confer any spatial information, i.e. how the pixels physically relate to one another in the image. This is normal because the main function of a histogram is to represent statistical information in a compact form. The frequency data can be used to calculate the minimum and maximum intensity values, the mean, and even the median.

This series will look at the various types of histograms, how they can be used to produce better pictures, and how they can be manipulated to improve the aesthetics of an image.