Image Processing Toolbox™

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. 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 histogram counts returned from imhist. By default, entropy uses two bins for logical arrays and 256 bins for uint8, uint16, or double arrays.

I can be a multidimensional image. If I has more than two dimensions, the entropy function treats it as a multidimensional grayscale image and not as an RGB image.

Class Support

I can be logical, uint8, uint16, or double and must be real, nonempty, and nonsparse. E is double.

Notes

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.

Examples

I = imread('circuit.tif'); 
J = entropy(I)

References

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

edgetaper

entropyfilt