IIEEJ Transactions on Image Electronics and Visual Computing
Online ISSN : 2188-1901
Print ISSN : 2188-1898
ISSN-L : 2188-191X
A Screen Shake Determination Method Based on 2D Motion Histogram Analyses by Using Group Transition and Maximum Group Ratio in Gaze Areas
Mei KODAMA
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2020 Volume 8 Issue 2 Pages 79-90

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Abstract

If videos have screen shake information, it is one of the important issues to prevent viewers from VIMS (visually induced motion sickness). So far there are two major approaches to prevent them. First approach is a visually sickness information extraction method by using bio-metric information, and since it is necessary to extract it after the physical condition becomes poor, the processing delay is inevitable. On the other hand, second approach is a motion information extraction method by using image processing in videos. However, it is reported that the processing time becomes longer when the detailed motion such as global motion estimation is analyzed. Thus, a screen shake determination method, which had used the block matching method as a simple motion analysis, motion direction histograms, and this similarity, had been proposed. However, there is still the problem that the accuracy of detecting screen shake decreases, when the amount of screen shake is small in the conventional method. It cannot extract the direction information of screen shake. To solve the problems, this paper proposes a novel screen shake determination method based on 2D motion histogram analyses. In particular, there are three features: the use of gaze areas, the group transition analysis of maximum frequency, and the maximum group ratio analysis in this method. A new evaluation value Ev is defined in consideration of both the accuracy of no-swing and pseudo swing images. Simulation experiments show that the Ev in the proposed method is at most 4.02 smaller than that in the conventional method for the small screen shake. Therefore, it is revealed that the proposed method improves the accuracy of detecting the small screen shake in the conventional method and can extract the direction information of screen shake. Furthermore, it is shown that it solves the problem of setting the threshold of the histogram correlation. An adaptive method for each gaze area, and an adaptive method for each number of directions and divisions in motion vector space will deserve for consideration, but they are left for further studies.

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© 2020 The Institute of Image Electronics Engineers of Japan
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