Full Search Algorithm for Block Matching in Image Processing

This code implements the full search algorithm for block matching in image processing, primarily used for estimating and calculating motion vectors. The algorithm works by systematically searching through all possible candidate blocks in the image and calculating their similarity with the reference block to identify the optimal matching block. The motion vector is then determined by comparing the displacement between the optimal matching block and the reference block. The full search algorithm employs techniques such as Sum of Absolute Differences (SAD) or Mean Squared Error (MSE) to evaluate similarity metrics between blocks. While being a straightforward and effective method that guarantees finding the globally optimal match, the algorithm requires exhaustive computation since it examines every possible block position within the search window. Consequently, in practical applications, this approach is often combined with faster algorithms like three-step search or diamond search to enhance matching efficiency and accuracy while maintaining reasonable computational overhead. Key implementation aspects include defining the search window size, block dimensions, and similarity threshold parameters to balance between precision and performance requirements. The code typically involves nested loops for exhaustive search operations and optimized memory access patterns for handling image data efficiently.