entropy

Entropy of grayscale image

Syntax

e = entropy(I)

Description

e = entropy(I) returns e, a scalar value representing the entropy of grayscale image I.

Examples

Calculate Entropy of Grayscale Image

Read image into the workspace.

I = imread('circuit.tif');

Calculate the entropy.

J = entropy(I)

J = 6.9439

Input Arguments

I — Grayscale image

numeric array

Grayscale image, specified as a numeric array of any dimension.

Data Types: double | uint8 | uint16 | uint32 | logical

Output Arguments

e — Entropy

numeric scalar

Entropy of image I, returned as a numeric scalar.

Data Types: double

More About

Entropy

Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image.

Entropy is defined as -sum(p.*log2(p)), where p contains the normalized histogram counts returned from imhist.

Tips

• By default, entropy uses two bins for logical arrays and 256 bins for uint8, uint16, or double arrays. entropy converts any class other than logical to uint8 for the histogram count calculation so that the pixel values are discrete and directly correspond to a bin value.

References

[1] Gonzalez, R. C., R. E. Woods, and S. L. Eddins. Digital Image Processing Using MATLAB. New Jersey, Prentice Hall, 2003, Chapter 11.