entropy

Entropy of grayscale image

Syntax

e = entropy(I)

Description

e = entropy(I) returns 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.

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 | logical array

Grayscale image, specified as a numeric array or logical array of any dimension. The entropy function expects images of data type double and single to have values in the range [0, 1]. If I has values outside the range [0, 1], then you can rescale values to the expected range by using the rescale function.

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

By default, entropy uses two bins for logical arrays and 256 bins for uint8, uint16, or double arrays. entropy converts any data type 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.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

entropy supports the generation of C and C++ code (requires MATLAB^® Coder™). For more information, see Code Generation for Image Processing.

Version History

Introduced before R2006a

R2023a: Support for C code generation

entropy now supports the generation of C code (requires MATLAB Coder).