On the code modernization of shared sampling alpha matting with OpenMP

Image matting separates the foreground object from a given image by estimating the alpha values. It has been one crucial step in computer vision, as well as an essential technique in visual effects in the modern filmmaking industry. Due to its time complexity being proportional to the size of the unknown region, the computational speed for processing image matting on high-resolution images has been traditionally slow. In this paper, we present the code modernization of Shared Sampling Alpha Matting (SSAM) algorithm with OpenMP to speed up the computation on multicore server platforms. Significant efforts are required due to the sequential nature of the code, with data dependencies and complex data structures in the original code. The implementation of migrating the SSAM code to multicore platforms and the experiments performed on several server platforms of different categories/specs are presented. Our experimental results demonstrate that the Alpha Matting using OpenMP increases performance significantly on Intel Xeon Phi Knights Landing (KNL) processors when processing high-resolution images. The code improvements we achieved can support a parallel application developer with high-performance goals. (C) 2019 Elsevier B.V. All rights reserved.