Our results validate our theoretical approach by showing that we can reduce transform computational complexity significantly while minimizing distortion.
The improved performance of the PFD over traditional filtering methods comes at the cost of increased transform and filtering complexity, although clever design decisions can be made to minimize the complexity increases.
By using the fast cosine transform algorithms, the complexity of DCT could be as low as O(n logn) and the total complexity of PSNR-HVS-M will be ≈ O(n2 n logn) = O(n3 log n).
In this paper, we use the vanishing point as a guide to decrease the Hough transform's space complexity.
After the real-valued transform, the computation complexity for each iteration will reduce much when the steering matrix is used to calculate.
There have been many recent approaches for reducing the computational complexity of the DCT transform, but the reduction in computational complexity comes at the cost of PSNR.
Whereas the number of the partial transformed outputs is equal to or smaller than 1/32 total FFT transform length, the arithmetic complexity of the proposed algorithm will be larger than the other pruning FFTs.
The capacity to merge theory with mechanistic understanding and empirical evidence, to assimilate diverse sources of information and to accommodate complexity will transform the collection and interpretation of data.
The control experiment shows that the improved Hough transform efficiently reduces space complexity.
