基于智能裁剪的低复杂度视频重定向算法

LOW-COMPLEXITY VIDEO RETARGETING BASED ON SMART CROPPING

  • 摘要: 提出一种基于智能剪裁的低复杂度视频重定向算法。该算法利用YOLO-Fastest对视频目标进行检测,并根据先验框面积、准确度与类型确定其重要度,以此指导剪裁窗口的选取;根据主体先验框的位置计算视频帧的相对位移,约束剪裁窗口的路径,以保护重定向视频的时间连续性。利用用户调查与两个客观质量评价指标(包括时域连续性失真量度和显著相似性量度)来评价算法的重定向性能,并与现有算法进行了时间消耗的比较。实验结果表明,在重定向视频的质量没有明显降低的前提下,该算法能大幅降低算法的复杂度,减少视频重定向处理的耗时。

     

    Abstract: A low-complexity video retargeting algorithm based on smart cropping is proposed. The YOLO-Fastest algorithm was used to detect video target, and the importance of prior box was determined according to area, accuracy, and type, to guide the selection of cropping window. The relative displacement of video frame was calculated according to the position of subject prior box, and the path of cropping window was constrained to protect temporal continuity of retargeted video. In this paper, a user study and two objective quality metrics (temporal continuity distortion measure and significant similarity measure) were used to evaluate retargeting performance, and time consumption was compared with existing algorithms. The experimental results show that the proposed algorithm can greatly reduce algorithm complexity and video retargeting time without obvious reduction of retargeting video quality.

     

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