## 6 Experimental Results
Arbitrary pixel loop unrolling and arbitrary kernel loop unrolling are used in place of pixel loop unrolling and kernel loop unrolling, respectively. Figure 17: The speedup ratio of randomly-kernel-subsampled bilateral filtering (RKS-BF) with respect to the kernel radius of FIR filtering. (**a**) 1/4 subsample in weight computation; (**b**) 1/16 subsample in weight computation; (**c**) 1/4 subsample in LUT; (**d**) 1/16 subsample in LUT. If the ratio exceeds 1, the given pattern is faster than kernel loop vectorization. LUTs. Together, these results indicate a special efficiency for proposed kernel loop vectorization in the kernel-adaptive sampling technique. Figure 9:0 The speedup ratio for randomly-kernel-subsample adaptive Gaussian filtering (RKS-AGF) with respect to the kernel radius of FIR filtering. (**a**) 1/4 subsample in weight computation; (**b**) 1/16 subsample in weight computation; (**c**) 1/4 subsample in LUT; (**d**) 1/16 subsample in LUT. If the ratio exceeds 1, the given pattern is faster than the kernel loop vectorization. Figure 9:1 Processing time for randomly-kernel-subsampled adaptive Gaussian filtering (RKS-AGF) with respect to the kernel radius of FIR filtering. (**a**) 1/4 subsample in weight computation; (**b**) 1/16 subsample in weight computation; (**c**) 1/4 subsample in LUT; (**d**) 1/16 subsample in LUT. Arbitrary pixel loop unrolling and arbitrary kernel loop unrolling are used in place of pixel loop unrolling and kernel loop unrolling, respectively. Figure 20 depicts the accuracies of GRF, BF, AGF, RKS-GRF, RKS-BF, and RKS-AGF. Here, we compare the results of a full sampling with the results of a subsampling using peak signal noise ratio (PSNR). In this figure, PSNR is found to be around 40 dB for all cases; thus, the filtered image has sufficient accuracy of approximation for all filters. Offset computing time for loop vectorization in data structure transformation is discussed. Figure 21 presents the processing time required for loop vectorization. Processing time increases with increases in kernel radius and image size. Computing time is linearly proportional to the number of elements in the loop-vectorized data, namely, \((2r+1)^{2}\times s\), where the kernel radius is \(r\) and the image size is \(s\). However, for interactive filtering, this drawback can be neglected. Rearranged processing time must occur before filtering can be done at first in the proposed pattern, but it is not required for subsequent filtering. Therefore, rearrangement of processing time does not lead to significant problems in interactive filtering. If images show continuous change, such as in video, the second-fastest pattern should be used instead of the proposed one. The pattern that is proposed requires rearrangement for every image. Figure 9:2 Processing time for loop vectorization with respect to the kernel radius of FIR filtering. There are \(2\times 4\) lines, and their combinations represent image resolution (\(512\times 512\) and \(900\times 750\)) and kernel subsampling ratio (full, \(1/4\), and \(1/16\)). Figure 9:3 PSNR with respect to kernel radius of FIR filtering. (**a**) Gaussian range filter; (**b**) Bilateral filter; (**c**) Adaptive Gaussian filter; (**d**) Randomly-kernel-subsampled Gaussian range filter; (**e**) Randomly-kernel-subsampled filter; (**f**) Randomly-kernel-subsampled adaptive Gaussian filter. Image size is \(512\times 512\).