MATLAB Code Implementation for Image Variance and Statistical Features

This document provides detailed explanations on implementing image variance, skewness, kurtosis, energy, and entropy using MATLAB. The implementation involves calculating statistical measures from image pixel values, where variance measures the degree of brightness variation in an image - higher values indicate more intense brightness changes. Skewness is obtained by computing image gradients, which helps quantify edge information within the image. Kurtosis is derived from analyzing the distribution of pixel values, indicating the concentration of peaks in the image. Energy represents the sum of squared pixel values, reflecting the energy distribution across the image. Finally, entropy is calculated through the probability distribution of pixel values, measuring the information content in the image.

For code implementation, variance can be computed using MATLAB's var() function applied to the image matrix after conversion to double precision. Skewness calculation utilizes the skewness() function on the flattened image array, while kurtosis employs the kurtosis() function. Energy implementation involves summing squared pixel values using sum(img(:).^2), and entropy calculation uses the entropy() function or custom implementation with histogram-based probability distribution. All these statistical features can be efficiently computed with simple MATLAB commands that directly operate on the image matrix.

The implementation code is straightforward and executable, producing corresponding results immediately. Each function typically requires only 1-2 lines of code, making this approach highly practical for image analysis tasks. The algorithms work with both grayscale and color images (by processing individual channels), and include necessary data type conversions for accurate computations.