Finding the optimal implementation of calculations is one of the most critical challenges in image processing programming. Halide is a domain-specific language for high-performance image processing. Its auto-scheduler is a helpful tool for solving this problem; however, its scheduling is not a panacea for complex flows. In this paper, we evaluate the performance of the auto-scheduler by comparing it to hand-manually implemented C++ codes. The algorithm used for comparison is Directional cubic convolution interpolation (DCCI), whose computation schedule is challenging to optimize. We evaluate three auto-schedulers: Adams et al. 's, Li et al.'s, and Mullapudi et al.s. Experimental results show that the performance of the schedule generated by Adams' method is comparable to that of the hand-implemented C++ code.
Writing image processing in WebAssembly enables the development of highly portable libraries. However, since WebAssembly is a new technology, there are not enough libraries available, and it is only supported by OpenCV. However, OpenCV has not been optimized specifically for WebAssembly. Therefore, in this paper, we develop native code using WebAssembly and compare it with OpenCV's Gaussian filter using the separable Gaussian filter, a classical acceleration method for Gaussian filters. Experimental results showed that the Separable Gaussian filter was faster than OpenCV by performing vector operations in WebAssembly.
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