AI

Deformable block based motion estimation in omnidirectional image sequences

Abstract

This paper presents an extension of block-based motion estimation for omnidirectional videos, based on a camera and translational object motion model that accounts for the spherical geometry of the imaging system. We use this model to design a new algorithm to perform block matching in sequences of panoramic frames that are the result of the equirectangular projection. Experimental results demonstrate that significant gains can be achieved with respect to the classical exhaustive block matching algorithm (EBMA) in terms of accuracy of motion prediction. In particular, average quality improvements up to approximately 6dB in terms of Peak Signal to Noise Ratio (PSNR), 0.043 in terms of Structural SIMilarity index (SSIM), and 2dB in terms of spherical PSNR, can be achieved on the predicted frames.