Significant Features Selection Resistant to Temporal Distortions

Abstract

Most patterns in continuous video sequences are similar. Temporal distortion, e.g. frames dropping, insertions, transposition, is a challenging issue for video reconstruction to find the actual missing positions in video sequences. The aim of this paper is to raise the detection accuracy and synchronize video frames back to original positions following temporal synchronization distortions. The successive video frames have similar statistics but the statistics in some local regions may differ from one another. Therefore, the block partition is partitioned into non-overlapping blocks by each frame, and then the local variance is calculated and taken as the block feature in each block. For most of the video frames, the pixels within the frame blocks are correlated and the maximum eigenvalue will be far from other eigenvalues. In this case, the maximum eigenvalue is set as the dominated block feature. For less correlated blocks, the values of the eigenvalues will be a little closer. In this case, the mean value of the eigenvalues represents the dominated block feature. Then, the sum of variance is regarded as the frame feature to calculate from these selective dominated blocks. Simulation results show the proposed methods are robust in evaluating the missing positions against temporal distortions.