A Truncation Method for Computing Walsh Transforms with Applications to Image Processing

We present a method called the Truncation method for computing Walsh-Hadamard transforms of one- and two-dimensional data. In one dimension, the method uses binary trees as a basis for representing the data and computing the transform. In two dimensions, the method uses quadtrees (pyramids), adaptive quad-trees, or binary trees as a basis. We analyze the storage and time complexity of this method in worst and general cases. The results show that the Truncation method degenerates to the Fast Walsh Transform (FWT) in the worst case, while the Truncation method is faster than the Fast Walsh Transform when there is coherence in the input data, as will typically be the case for image data. In one dimension, the performance of the Truncation method for N data samples is between O(N) and O(N log₂N), and it is between O(N²) and O(N² log₂N) in two dimensions. Practical results on several images are presented to show that both the expected and actual overall times taken to compute Walsh transforms using the Truncation method are less than those required by a similar implementation of the FWT method.