Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

Abstract

Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.