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Modelling the non-stationary videos for performance assessment of frame reconstruction

机译:建模非平稳视频以评估帧重建的性能

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Usually public datasets involving non-stationary video sequences do not have the corresponding ground-truth stationary videos. This means that the performance assessment of reconstructed videos using the wide spread full-reference metrics is impossible. For this purpose, we propose a set of models simulating the shaking camera motion in real-life applications, such as the hand-held shooting and shooting from unmanned aerial vehicles and moving platforms. First, the stationary video sequence is analyzed carefully. 3D camera path is built using the pseudo-disparity values between the consecutive frames based on the corresponding feature points. This high cost procedure permits to build a relatively smooth camera path in 3D space with the non-significant jitters. Second, the end-user chooses the duration, position, and type of shake and/or jitters in video sequence. Third, the desired simulation is implemented in wavelet domain. The distortions, such as displacements, rotations, and local/global blurring, are simulated depending on the chosen situation. We use the physically-based and sample models. In order to reach the ground-truth demonstration, we crop the original frames. If RGB-D video is available, then realistic perspective projections of shaking synthesized video can be created.
机译:通常,涉及非平稳视频序列的公共数据集没有相应的地面静止视频。这意味着不可能使用广泛使用的全参考指标对重建视频进行性能评估。为此,我们提出了一组模型来模拟实际应用中的抖动相机运动,例如手持拍摄以及从无人机和移动平台拍摄。首先,对固定视频序列进行仔细分析。使用基于相应特征点的连续帧之间的伪视差值构建3D相机路径。这种高成本的过程允许在3D空间中建立相对平滑的相机路径,并且不会产生明显的抖动。其次,最终用户选择视频序列中抖动和/或抖动的持续时间,位置和类型。第三,在小波域中实现所需的仿真。根据所选情况模拟变形,例如位移,旋转和局部/全局模糊。我们使用基于物理的模型和样本模型。为了达到真实的演示效果,我们裁剪了原始框架。如果RGB-D视频可用,则可以创建摇动合成视频的逼真的透视投影。

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