Image Quadtree Partitioning Algorithm Implementation

The following code demonstrates quadtree partitioning of digital images. Quadtree decomposition is an efficient image processing technique that recursively divides images into quadrants to enable better analysis and manipulation. While various quadtree partitioning methods exist, this implementation adopts a classical approach based on homogeneity criteria. Notably, quadtree segmentation has broad applications beyond image processing, including geographic information systems (GIS) and spatial data indexing. The implementation comprises several key steps: 1. Tree Construction: The algorithm builds a quadtree structure where each node represents an image region, with leaf nodes containing homogeneous blocks 2. Region Splitting: Uses recursive division when a region's pixels exceed predefined variance thresholds 3. Block Processing: Implements efficient boundary handling and memory management for large images Key functions include: - qt_decomposition(): Main function that initiates the partitioning process - check_homogeneity(): Evaluates regional uniformity using standard deviation metrics - split_region(): Recursively divides non-homogeneous regions into four quadrants The code provides detailed comments explaining each processing stage, making it suitable for educational purposes and practical image analysis applications.