A Multi-scale Evaluation Method for Motion Filtering in Digital Image Stabilization

Zhai, Bo; Zheng, Jiangbin; Wang, Yue; Zhang, Chi

doi:10.1109/ictai.2015.103

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Liu et al [6] suggested an empirical process that assumes that if the more energy is in the low-frequency part of a video, the video will be more stable. We see that most existing proposed metrics [1,2,21,25,26] for VS approaches are either synthetically shaking video-based or heuristic approaches [2,6,24,27].…”

Section: Related Workmentioning

confidence: 96%

“…From the ground truth motion and the estimated motion, the researchers computed a mean square error (MSE) between consecutive frames and root mean square (RMS) error for the whole sequences. Zhai et al [26] took a threshold to categorize a high-frequency component (jitter) and a lowfrequency component (divergence) and decomposed these jitters and divergence utilizing a high-pass filter and low-pass filter. Furthermore, synthetic jitters were added to a stable video to get an unstable video, and finally the researchers assessed the stability of the video stabilization algorithm based on the MSE of the reference path and the processed path.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A novel accuracy assessment model for video stabilization approaches based on background motion

Hossain¹,

Nguyen²,

Huh³

2019

Turk J Elec Eng & Comp Sci

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In this paper, we propose a new accuracy measurement model for the video stabilization method based on background motion that can accurately measure the performance of the video stabilization algorithm. Undesired residual motion present in the video can quantitatively be measured by the pixel by pixel background motion displacement between two consecutive background frames. First of all, foregrounds are removed from a stabilized video, and then we find the two-dimensional flow vectors for each pixel separately between two consecutive background frames. After that, we calculate a Euclidean distance between these two flow vectors for each pixel one by one, which is regarded as a displacement of each pixel. Then a total Euclidean distance of each frame is averaged to get a mean displacement for each pixel, which is called mean displacement error, and finally we calculate the average mean displacement error. Our experimental results show the effectiveness of our proposed method.

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