This paper presents a novel local motion estimation algorithm for omnidirectional images. The algorithm captures correlation between two spherical images of a scene, taken from arbitrary viewpoints, with the objective to reduce the encoding rate of these images. It first performs a multiresolution decomposition of the spherical images, in order to improve the consistency of the motion estimation, with a limited computational complexity. Then, it determines pairs of similar solid angles and matches blocks of the two omnidirectional images, directly in the spherical domain. This approach allows a simple motion estimation implementation, that avoids potential discrepancies induced while unfolding omnidirectional images to implement a classical motion estimation on images. The proposed algorithm is shown to provide a quite efficient image prediction, and the prediction error is almost exclusively composed of high frequency noise.
Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert, Athanasios Fitsios
Rachid Guerraoui, Martin Jaggi, Youssef Allouah, Anastasiia Koloskova, Aymane El Firdoussi