Video compression using hybrid hexagon search and teaching-learning-based optimization technique for 3D reconstruction

Motion estimation from a video sequence is an interesting issue in video processing. Nowadays, research has been focused on global optimization techniques, that estimate the optical flow for pixel neighborhoods. In this paper, a hybrid statistically effective motion estimation procedure has been proposed for better effectiveness video compression. This method explores by utilizing a hexagonal search pattern with a secure number of search points at every lattice. It uses the association among bordering pixels within the frame. So as to diminish the computative intricacy, this methodology uses hybrid hexagon search and teaching-learning based optimization algorithm. This method additionally decreases the computational unpredictability of block matching procedure. The image quality has been confirmed through 3D reconstruction using structured light techniques. This strategy has been contrasted with different existing strategies and hereby utilizing the hexagon search-based teaching-learning optimization algorithm could get a higher precision interms of PSNR of 44.36%, MSE of 2.40 and compression ratio of 7.50.