MATLAB Implementation for Calculating Binary Image Entropy and Cross-Entropy

This MATLAB program calculates information entropy and cross-entropy for binary images. The implementation involves computing probability distributions of pixel values (typically 0 and 1 for binary images) using histogram functions, then applying the entropy formula H = -Σ p(i)log2(p(i)) where p(i) represents the probability of each pixel value. For cross-entropy calculation, the program compares two distributions using H(P,Q) = -Σ P(i)log2(Q(i)). By integrating these entropy-based methods with segmentation algorithms, more accurate image segmentation results can be achieved, thereby enhancing overall image processing effectiveness. The entropy calculation module can function as an evaluation standard to assess image complexity and information content. Through entropy and cross-entropy analysis, researchers can gain better understanding of image characteristics and content. Key functions implemented include: probability distribution estimation using histcounts(), entropy calculation with custom log-based functions, and cross-entropy computation between reference and target distributions. This computational program holds significant application value in the field of image processing, particularly for quality assessment and segmentation validation.