Content removal via both thresholding averaging and two dimensional discrete fractional Fourier transform
Bingo Wing‐Kuen Ling, Yaru Wang, Zhijing Yang, Nain Cai · 2015
This paper proposes a novel content removal technique for enhancing the camera identification performance. Here, very low bit rate videos with the overall noise patterns having time varying statistics are considered. First, different two dimensional discrete fractional Fourier transforms with different rotational angles are applied to the overall noise pattern of each frame of each video. Second, the modulus of each element of each transformed matrix is normalized to one if the rotational angles of the transforms are not equal to the integer multiples of π. Third, the corresponding two dimensional inverse discrete fractional Fourier transform is applied to each normalized matrix and the corresponding real part is taken out for the further processing. Fourth, the absolute values of the elements in each normalized real valued matrix are bounded by a certain threshold value. Finally, the processed matrices are averaged over all the rotational angles and all the frames of the videos corresponding to the same camera. Extensive computer numerical simulation results on the correlation performances are presented. It is found that the proposed method outperforms the existing method for a wide range of rotational angles.